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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Almeida, A., Menini, N., Verbesselt, J. & da S. Torres, R. BFAST Explorer: An Effective Tool for Time Series Analysis 2018 IEEE International Geoscience and Remote Sensing Symposium  inproceedings  
    Abstract: The use of remote sensing images has been broadly employed over the past decades in order to detect and investigate temporal changes on the Earth surface. This is one of the main goals of the widely used Breaks For Additive Season and Trend (BFAST) method. In this paper, we introduce the BFAST, an effective open-source tool for time series analysis based on BFAST, which is ready to be used at a personal webpage. We present its functional and architectural design, as well as a common usage scenario. Moreover, this tool was well received by the target community, notably by its recently integration into a private cloud computing platform.
    BibTeX:
    @inproceedings{Almeida2018IGARSS-accepted,
      author = {Alexandre Almeida and Nathalia Menini and Jan Verbesselt and Ricardo da S. Torres},
      title = {BFAST Explorer: An Effective Tool for Time Series Analysis},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
      year = {2018}
    }
    
    Santana, T.M.H.C., da S. Torres, R. & dos Santos, J.A. Superpixel Context Description based on Visual Words Co-occurrence Matrix 2018 IEEE International Geoscience and Remote Sensing Symposium  inproceedings  
    Abstract: In this paper, we introduce a novel representation to encode contextual information in object-based remote sensing image classification problems. The solution relies on the creation of a visual codebook and its use to compute the co-occurrence of visual words within a superpixel and within its neighboring regions. Performed experiments on the well-known collections (grssdfc2014 and ISPRS Potsdam) demonstrate that the proposed approach is effective, yielding comparable or better results than several baselines.
    BibTeX:
    @inproceedings{Santana2018IGARSS-accepted,
      author = {Tiago M. H. C. Santana and Ricardo da S. Torres and Jefersson A. dos Santos},
      title = {Superpixel Context Description based on Visual Words Co-occurrence Matrix},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
      year = {2018}
    }
    
    Werneck, R., Dourado, I.C., Fadel, S., Tabbone, S. & da S. Torres, R. Graph-based Early-fusion for Flood Detection 2018 IEEE International Conference on Image Processing  inproceedings  
    Abstract: Flooding is one of the most harmful natural disasters, as it poses danger to both buildings and human lives. Therefore, it is fundamental to monitor these disasters to define prevention strategies and help authorities in damage control. With the wide use of portable devices (e.g., smartphones), there is an increase of the documentation and communication of flood events in social media. However, the use of these data in monitoring systems is not straightforward and depends on the creation of effective recognition strategies. In this paper, we propose a fusion-based recognition system for detecting flooding events in images extracted from social media. We propose two new graph-based early-fusion methods, which consider multiple descriptions and modalities to generate an effective image representation. Our results demonstrate that the proposed methods yield better results than a traditional early-fusion method and a specialized deep neural network fusion solution.
    BibTeX:
    @inproceedings{Werneck2018ICIP-accepted,
      author = {Rafael Werneck and Icaro C. Dourado and Samuel Fadel and Salvatore Tabbone and Ricardo da S. Torres},
      title = {Graph-based Early-fusion for Flood Detection},
      booktitle = {IEEE International Conference on Image Processing},
      year = {2018}
    }
    
    Boulkenafet, Z., Komulainen, J., Akhtar, Z., Benlamoudi, A., Samai, D., Bekhouche, S.E., Ouafi, A., Dornaika, F., Taleb-Ahmed, A., Qin, L., Peng, F., Zhang, L.B., Long, M., Bhilare, S., Kanhangad, V., Costa-Pazo, A., Vazquez-Fernandez, E., Perez-Cabo, D., Moreira-Perez, J.J., Gonzalez-Jimenez, D., Mohammadi, A., Bhattacharjee, S., Marcel, S., Volkova, S., Tang, Y., Abe, N., Li, L., Feng, X., Xia, Z., Jiang, X., Liu, S., Shao, R., Yuen, P.C., Almeida, W.R., Andalo, F., Padilha, R., Bertocco, G., Dias, W., Wainer, J., da S. Torres, R., Rocha, A., Angeloni, M.A., Folego, G., Godoy, A. & Hadid, A. A competition on generalized software-based face presentation attack detection in mobile scenarios 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 688-696  inproceedings DOI  
    Abstract: In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.
    BibTeX:
    @inproceedings{Boulkenafet2017IJCB,
      author = {Z. Boulkenafet and J. Komulainen and Z. Akhtar and A. Benlamoudi and D. Samai and S. E. Bekhouche and A. Ouafi and F. Dornaika and A. Taleb-Ahmed and L. Qin and F. Peng and L. B. Zhang and M. Long and S. Bhilare and V. Kanhangad and A. Costa-Pazo and E. Vazquez-Fernandez and D. Perez-Cabo and J. J. Moreira-Perez and D. Gonzalez-Jimenez and A. Mohammadi and S. Bhattacharjee and S. Marcel and S. Volkova and Y. Tang and N. Abe and L. Li and X. Feng and Z. Xia and X. Jiang and S. Liu and R. Shao and P. C. Yuen and W. R. Almeida and F. Andalo and R. Padilha and G. Bertocco and W. Dias and J. Wainer and Ricardo da S. Torres and A. Rocha and M. A. Angeloni and G. Folego and A. Godoy and A. Hadid},
      title = {A competition on generalized software-based face presentation attack detection in mobile scenarios},
      booktitle = {IEEE International Joint Conference on Biometrics (IJCB)},
      year = {2017},
      pages = {688-696},
      doi = {http://dx.doi.org/10.1109/BTAS.2017.8272758}
    }
    
    Domingues, D.G., Funghetto, S., Miranda, M.R., Batista, P.K.C.M., de Oliveira, P.R.F., Assis, G.A., da Rocha, A.F. & da S. Torres, R. Mobility and freedom: Affective cane for expanded sensorium and embodied cognition 2017 23rd International Conference on Virtual System Multimedia (VSMM), pp. 1-7  inproceedings DOI  
    Abstract: Nowadays, cognitive computing systems and embodied cognition associated with the availability of low cost sensors and their integration into common consumer products constitutes an opportunity to redefine the live of individuals in new ways, both in the depth and the richness of detail. The resulting flood of data will require the development of techniques of analysis and visualization at the same time as it opens new opportunities to improve the lives of individuals. In this work, we make demonstrable progress on both of these objectives, by developing cognitive computing related to ethnographic methodologies and strategies for practices with the objective of promoting health and wellbeing. We introduce an affective cane prototype, which has been designed to enable the active participation of people with disability or reduced mobility supporting their autonomy, independence, improved quality of life, and social integration.
    BibTeX:
    @inproceedings{Domingues2017VSMM,
      author = {Diana G. Domingues and Silvana Funghetto and Mateus R. Miranda and Pedro K. C. M. Batista and Paulo R. F. de Oliveira and Gilda A. Assis and Adson F. da Rocha and Ricardo da S. Torres},
      title = {Mobility and freedom: Affective cane for expanded sensorium and embodied cognition},
      booktitle = {23rd International Conference on Virtual System Multimedia (VSMM)},
      year = {2017},
      pages = {1-7},
      doi = {http://dx.doi.org/10.1109/VSMM.2017.8346248}
    }
    
    Hernández-Albarracín, J.F., Ferreira, E., dos Santos, J.A. & da S. Torres, R. Fusion of Genetic-Programming-Based Indices in Hyperspectral Image Classification Tasks 2017 IEEE International Geoscience and Remote Sensing Symposium, pp. 554-557  inproceedings DOI  
    Abstract: This paper introduces a two-step hyper- and multi-spectralimage classification approach. The first step relies on the useof a genetic programming (GP) framework to both select andcombine appropriate bands. The second step is concernedwith the image classification itself. We present two strategiesfor multi-class classification problems based on the combinationof GP-based indices defined in binary classificationscenarios. Performed experiments involving well-known andwidely-used datasets demonstrate that the proposed approachyields comparable or better effectiveness performance whencompared to several traditional baselines.
    BibTeX:
    @inproceedings{Hernandez-Albarracin2017IGARSS,
      author = {Juan F. Hernández-Albarracín and Edemir Ferreira and Jeferson Alex dos Santos and Ricardo da S. Torres},
      title = {Fusion of Genetic-Programming-Based Indices in Hyperspectral Image Classification Tasks},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
      year = {2017},
      pages = {554-557},
      doi = {http://dx.doi.org/10.1109/IGARSS.2017.8127013}
    }
    
    Mariano, G., Soares, N.C., Morellato, L.P. & da Silva Torres, R. Change Frequency Heatmaps for Temporal Multivariate Phenological Data Analysis 2017 IEEE 13th International Conference on e-Science, pp. 305-314  inproceedings DOI  
    Abstract: The huge amount of multivariate temporal data that has been produced in several applications demands the creation of appropriate tools for the analysis and pattern characterization of change. This paper introduces a novel image-based representation, named Change Frequency Heatmap (CFH), to encode temporal changes of multivariate numerical data. The method computes histograms of change patterns observed at successive timestamps. We validate the use of CFHs through the creation of a temporal change characterization tool to support complex plant phenology analysis, concerning the characterization of plant life cycle changes of multiple individuals and species over time. We demonstrate the potential of CFH to support visual identification of complex temporal change patterns, especially to decipher interindividual variations in plant phenology.
    BibTeX:
    @inproceedings{Mariano2017eScience,
      author = {Greice Mariano and Natalia Costa Soares and Leonor Patrícia Morellato and Ricardo da Silva Torres},
      title = {Change Frequency Heatmaps for Temporal Multivariate Phenological Data Analysis},
      booktitle = {IEEE 13th International Conference on e-Science},
      year = {2017},
      pages = {305-314},
      doi = {http://dx.doi.org/10.1109/eScience.2017.44}
    }
    
    Nogueira, K., dos Santos, J.A., Cancian, L., Borges, B.D., Silva, T.S.F., Morellato, L.P. & da S. Torres, R. Semantic Segmentation of Vegetation Images Acquired by Unmanned Aerial Vehicles Using an Ensemble of Convnets 2017 IEEE International Geoscience and Remote Sensing Symposium, pp. 3787-3790  inproceedings DOI  
    Abstract: Vegetation segmentation in high resolution images acquiredby unmanned aerial vehicles (UAVs) is a challenging taskthat requires methods capable of learning high-level featureswhile dealing with fine-grained data. In this paper, we proposea combination of different methods of semantic segmentationbased on Convolutional Networks (ConvNets) to obtainhighly accurate segmentation of individuals of differentvegetation species. The objective is not only to learn specificand adaptable features depending on the data, but alsoto learn and combine appropriate classifiers. We conducteda systematic evaluation using a high-resolution UAV-basedimage dataset related to a campo rupestre vegetation in theBrazilian Cerrado biome. Experimental results show that theensemble technique overcomes all segmentation strategies.
    BibTeX:
    @inproceedings{Nogueira2017IGARSS,
      author = {Keiller Nogueira and Jefersson A. dos Santos and Leonardo Cancian and Bruno D. Borges and Thiago S. F. Silva and Leonor Patricia Morellato and Ricardo da S. Torres},
      title = {Semantic Segmentation of Vegetation Images Acquired by Unmanned Aerial Vehicles Using an Ensemble of Convnets},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
      year = {2017},
      pages = {3787-3790},
      doi = {http://dx.doi.org/10.1109/IGARSS.2017.8127824}
    }
    
    Rodrigues, D.C.U.M., Moura, F.A., Cunha, S.A. & da S. Torres, R. Visualizing Temporal Graphs Using Visual Rhythms: A Case Study in Soccer Match Analysis 2017
    Vol. 38th International Conference on Information Visualization Theory and Applications (IVAPP 2017), pp. 96-107 
    inproceedings DOI  
    Abstract: In several applications, a huge amount of graph data have been generated, demanding the creation of appropriate tools for graph visualization. One class of graph data which is attracting a lot of attention recently are the temporal graphs, which encode how objects and their relationships evolve over time. This paper introduces the Graph Visual Rhythm, a novel image-based representation to visualize changing patterns typically found in temporal graphs. The use of visual rhythms is motivated by its capacity of providing a lot of contextual information about graph dynamics in a compact way. We validate the use of graph visual rhythms through the creation of a visual analytics tool to support the decision-making process based on complex-network-oriented soccer match analysis.
    BibTeX:
    @inproceedings{Rodrigues2017IVAPP,
      author = {Daniele C. Uchoa Maia Rodrigues and Felipe A. Moura and Sergio Augusto Cunha and Ricardo da S. Torres},
      title = {Visualizing Temporal Graphs Using Visual Rhythms: A Case Study in Soccer Match Analysis},
      booktitle = {8th International Conference on Information Visualization Theory and Applications (IVAPP 2017)},
      year = {2017},
      volume = {3},
      pages = {96-107},
      doi = {http://dx.doi.org/10.5220/0006153000960107}
    }
    
    Hernández-Albarracín, J.F., dos Santos, J.A. & da Silva Torres, R. Learning to Combine Spectral Indices with Genetic Programming 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, pp. 408-415  inproceedings DOI URL 
    Abstract: This paper introduces a Genetic Programming-based method for band selection and combination, aiming to support remote sensing image classification tasks. Relying on ground-truth data, our method selects spectral bands and finds the arithmetic combination of those bands (i.e., spectral index) that best separates examples ofive different classes. Experimental results demonstrate that the proposed method is very effective in pixel-wise binary classification problems.
    BibTeX:
    @inproceedings{Hernandez-Albarracin2016SIBGRAPI,
      author = {Juan Felipe Hernández-Albarracín and Jefersson Alex dos Santos and Ricardo da Silva Torres},
      title = {Learning to Combine Spectral Indices with Genetic Programming},
      booktitle = {29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016},
      year = {2016},
      pages = {408--415},
      url = {10.1109/SIBGRAPI.2016.063},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2016.063}
    }
    
    Mariano, G., Staggemeier, V.G., Morellato, L.P., da S. Torres, R. & Alberton, B. A Visual Rhythm Approach to Visualize Multidimensional Cyclical Data: A Case Study on Phenological Data Analysis 2016 International Conference on Digital Libraries  inproceedings  
    Abstract: Phenology is an important science that investigates the recurrent life cycles from living beings (plants and animals) and their relations to environmental changes. Traditional phenology studies typically are based on observations made by experts in the field over time and usually are correlated with climate data collected by weather sensors. In more recent studies, phenology experts have also been using images to monitor vegetation remotely. Although several approaches have been proposed for visualizing time series associated with phenology data, they are not suitable due to the lack of mechanisms: (i) to handle long-term series with many variables of different data types, and (ii) to identify cyclical temporal patterns using both images and numerical data. In this paper, we present a novel approach to visualize phenological data by combining radial visual structures along with visual rhythms. Radial visual structures are used to provide contextual insights regarding cyclical phenomena, while the visual rhythm encoding is used to summarize long-term time series into compact representations. Our objective is to support the discovery process by allowing the analysis of multidimensional cyclical data. This paper also discusses the use of the proposed visual structures in a case study concerning the analysis of data produced in the context of the e-phenology Project.
    BibTeX:
    @inproceedings{Mariano2016ICDL,
      author = {Greice Mariano and Vanessa G. Staggemeier and Leonor Patrícia Morellato and Ricardo da S. Torres and Bruna Alberton},
      title = {A Visual Rhythm Approach to Visualize Multidimensional Cyclical Data: A Case Study on Phenological Data Analysis},
      booktitle = {International Conference on Digital Libraries},
      year = {2016}
    }
    
    Nogueira, K., dos Santos, J.A., Fornazari, T., Silva, T.S.F., Morellato, L.P.C. & da S. Torres, R. Towards vegetation species discrimination by using data-driven descriptors 2016 9th IAPR Workshop on Pattern Recognition in Remote Sensing, pp. 1-6  inproceedings DOI  
    Abstract: In this paper, we analyse the use of Convolutional Neural Networks (CNNs or ConvNets) to discriminate vegetation species with few labelled samples. To the best of our knowledge, this is the first work dedicated to the investigation of the use of deep features in such task. The experimental evaluation demonstrate that deep features significantly outperform well-known feature extraction techniques. The achieved results also show that it is possible to learn and classify vegetation patterns even with few samples. This makes the use of our approach feasible for real-world mapping applications, where it is often difficult to obtain large training sets.
    BibTeX:
    @inproceedings{Nogueira2016PRRS,
      author = {Keiller Nogueira and Jefersson Alex dos Santos and Tamires Fornazari and Thiago S. F. Silva and Leonor Patricia C. Morellato and Ricardo da S. Torres},
      title = {Towards vegetation species discrimination by using data-driven descriptors},
      booktitle = {9th IAPR Workshop on Pattern Recognition in Remote Sensing},
      year = {2016},
      pages = {1--6},
      doi = {http://dx.doi.org/10.1109/PRRS.2016.7867024}
    }
    
    Muñoz, J.A.V., da Silva Torres, R. & Gonçalves, M.A. A Soft Computing Approach for Learning to Aggregate Rankings 2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM, pp. 83-92  inproceedings DOI URL 
    Abstract: This paper presents an approach to combine rank aggregation techniques using a soft computing technique -- Genetic Programming -- in order to improve the results in Information Retrieval tasks. Previous work shows that by combining rank aggregation techniques in an agglomerative way, it is possible to get better results than with individual methods. However, these works either combine only a small set of lists or are performed in a completely ad-hoc way. Therefore, given a set of ranked lists and a set of rank aggregation techniques, we propose to use a supervised genetic programming approach to search combinations of them that maximize effectiveness in large search spaces. Experimental results conducted using four datasets with different properties show that our proposed approach reaches top performance in most datasets. Moreover, this cross-dataset performance is not matched by any other baseline among the many we experiment with, some being the state-of-the-art in learning-to-rank and in the supervised rank aggregation tasks. We also show that our proposed framework is very efficient, flexible, and scalable.
    BibTeX:
    @inproceedings{Munoz2015CIKM,
      author = {Javier Alvaro Vargas Muñoz and Ricardo da Silva Torres and Marcos André Gonçalves},
      title = {A Soft Computing Approach for Learning to Aggregate Rankings},
      booktitle = {Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM},
      year = {2015},
      pages = {83--92},
      url = {http://doi.acm.org/10.1145/2806416.2806478},
      doi = {http://dx.doi.org/10.1145/2806416.2806478}
    }
    
    Nicastro, F., Pereira, R., Alberton, B., Morellato, L.P.C., Baranauskas, C. & da Silva Torres, R. A Semiotic-informed Approach to Interface Guidelines for Mobile Applications - A Case Study on Phenology Data Acquisition 2015
    Vol. Volume 3Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS), pp. 34-43 
    inproceedings DOI URL 
    Abstract: Portable devices have been experimented for data acquisition in different domains, e.g., logistics and census data acquisition. Nevertheless, their large-scale adoption depends on the development of effective applications with a careful interaction design. In this paper, we revisit existing interface design strategies and propose a guideline composed of semiotic-informed rules and questions for mobile user interface design. We demonstrate the use of the guideline in the evaluation of mobile application interfaces proposed for phenological data acquisition in the field.
    BibTeX:
    @inproceedings{Nicastro2015ICEIS,
      author = {Flavio Nicastro and Roberto Pereira and Bruna Alberton and Leonor Patricia C. Morellato and Cecilia Baranauskas and Ricardo da Silva Torres},
      title = {A Semiotic-informed Approach to Interface Guidelines for Mobile Applications - A Case Study on Phenology Data Acquisition},
      booktitle = {Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS)},
      year = {2015},
      volume = {Volume 3},
      pages = {34--43},
      url = {http://dx.doi.org/10.5220/0005379600340043},
      doi = {http://dx.doi.org/10.5220/0005379600340043}
    }
    
    Okada, Cé.Y., Pedronette, D.C.G. & da Silva Torres, R. Unsupervised Distance Learning by Rank Correlation Measures for Image Retrieval 2015 Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 331-338  inproceedings DOI URL 
    Abstract: Ranking accurately collection images is the main objective of Content-based Image Retrieval (CBIR) systems. In fact, the set of images ranked at the first positions generally defines the effectiveness of provided search services, i.e., they are used for assessing automatically the quality of search systems as this set usually contains the collection images that are of interest. Recently, the use of ranking information (e.g., rank correlation) has been used in different research initiatives with the objective of improving the effectiveness of image retrieval tasks. This paper presents a broad rank correlation analysis for unsupervised distance learning on image retrieval tasks. Various well-known rank correlation measures are considered and two new measures are proposed. Several experiments were conducted considering various image datasets involving shape, color, and texture descriptors. Experimental results demonstrate that ranking information can be exploited for distance learning tasks successfully. Evaluated approaches yield better results in terms of effectiveness than various state-of-the-art algorithms.
    BibTeX:
    @inproceedings{Okada2015ICMR,
      author = {César Yugo Okada and Daniel C. G. Pedronette and Ricardo da Silva Torres},
      title = {Unsupervised Distance Learning by Rank Correlation Measures for Image Retrieval},
      booktitle = {Proceedings of the 5th ACM on International Conference on Multimedia Retrieval},
      publisher = {ACM},
      year = {2015},
      pages = {331--338},
      url = {http://doi.acm.org/10.1145/2671188.2749335},
      doi = {http://dx.doi.org/10.1145/2671188.2749335}
    }
    
    Pedronette, D.C.G. & da Silva Torres, R. Unsupervised Effectiveness Estimation for Image Retrieval Using Reciprocal Rank Information 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 321-328  inproceedings DOI URL 
    Abstract: In this paper, we present an unsupervised approach for estimating the effectiveness of image retrieval results obtained for a given query. The proposed approach does not require any training procedure and the computational efforts needed are very low, since only the top-k results are analyzed. In addition, we also discuss the use of the unsupervised measures in two novel rank aggregation methods, which assign weights to ranked lists according to their effectiveness estimation. An experimental evaluation was conducted considering different datasets and various image descriptors. Experimental results demonstrate the capacity of the proposed measures in correctly estimating the effectiveness of different queries in an unsupervised manner. The linear correlation between the proposed and widely used effectiveness evaluation measures achieves scores up to 0.86 for some descriptors.
    BibTeX:
    @inproceedings{Pedronette2015SIBGRAPI,
      author = {Daniel C. G. Pedronette and Ricardo da Silva Torres},
      title = {Unsupervised Effectiveness Estimation for Image Retrieval Using Reciprocal Rank Information},
      booktitle = {28th SIBGRAPI Conference on Graphics, Patterns and Images},
      year = {2015},
      pages = {321--328},
      url = {http://dx.doi.org/10.1109/SIBGRAPI.2015.28},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2015.28}
    }
    
    Pinto-Cáceres, S.M., Almeida, J., Baranauskas, M.C.C. & da Silva Torres, R. FISIR: A Flexible Framework for Interactive Search in Image Retrieval Systems 2015
    Vol. Part {I}MultiMedia Modeling - 21st International Conference, MMM, pp. 335-347 
    inproceedings DOI URL 
    Abstract: This paper presents a flexible framework for interactive search in image retrieval systems. Our approach allows for the visual structure change in order to produce coherent layouts, which highlight the most relevant results according to user needs. This innovative framework is flexible in the sense it supports the dynamic creation of several hybrid visual designs, based on the combination of different visualization strategies. Results from a subjective evaluation demonstrate that the dynamic hybrid layouts created by the proposed framework provide the end-users with an effective user interface for intuitive browsing and searching experience.
    BibTeX:
    @inproceedings{Pinto-Caceres2015MMM,
      author = {Sheila M. Pinto-Cáceres and Jurandy Almeida and Maria Ceclia Calani Baranauskas and Ricardo da Silva Torres},
      title = {FISIR: A Flexible Framework for Interactive Search in Image Retrieval Systems},
      booktitle = {MultiMedia Modeling - 21st International Conference, MMM},
      year = {2015},
      volume = {Part I},
      pages = {335--347},
      url = {http://dx.doi.org/10.1007/978-3-319-14445-0_29},
      doi = {http://dx.doi.org/10.1007/978-3-319-14445-0_29}
    }
    
    Pisani, F., Pedronette, D.C.G., da Silva Torres, R. & Borin, E. Improving the Performance of the Contextual Spaces Re-Ranking Algorithm on Heterogeneous Systems 2015 XVI Simpůsio em Sistemas Computacionais de Alto Desempenho (WSCAD)  inproceedings  
    Abstract: Re-ranking algorithms have been proposed to improve the effectiveness of Content-Based Image Retrieval (CBIR) systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how we improved the efficiency of one of these algorithms, called Contextual Spaces Re-Ranking. We propose a modification to the algorithm that reduces its execution time by 1.6 x on average and improves its accuracy in most of our test cases. We also parallelized the implementation with OpenCL to use the CPU and GPU of an Accelerated Processing Unit (APU). Employing these devices to run different parts of the code resulted in speedups that range from 3.3 x to 4.2 x in comparison with the total execution time of the serial version.
    BibTeX:
    @inproceedings{Pisani2015WSCAD,
      author = {Flavia Pisani and Daniel C. G. Pedronette and Ricardo da Silva Torres and Edson Borin},
      title = {Improving the Performance of the Contextual Spaces Re-Ranking Algorithm on Heterogeneous Systems},
      booktitle = {XVI Simpůsio em Sistemas Computacionais de Alto Desempenho (WSCAD)},
      year = {2015}
    }
    
    da Silva Torres, R. Reasoning for Complex Data: Research Initiatives on Data Science 2015 Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, pp. 5-5  inproceedings DOI URL 
    Abstract: The exponential growth of digital information production and dissemination, as well as its huge diversification, introduce considerable management and access challenges. At the same time, new opportunities have been created by the development and use of analysis methods for the discovery of interesting and potentially useful patterns, present in the data sets. In this lecture, I will introduce research initiatives at Unicamp concerning the management of large volumes of Complex Data. The term Complex Data is associated with the universe of unstructured, semi-structured, and multimodal data, and therefore covers a wide variety of data as text, sound, image, video, geographic information, among others. This talk will show the versatility of ongoing research initiatives in three important applications: Information Retrieval, Multimedia Classification, and e-Science.
    BibTeX:
    @inproceedings{Torres2015Webmedia,
      author = {Ricardo da Silva Torres},
      title = {Reasoning for Complex Data: Research Initiatives on Data Science},
      booktitle = {Proceedings of the 21st Brazilian Symposium on Multimedia and the Web},
      publisher = {ACM},
      year = {2015},
      pages = {5--5},
      url = {http://doi.acm.org/10.1145/2820426.2822360},
      doi = {http://dx.doi.org/10.1145/2820426.2822360}
    }
    
    Valem, L.P., Pedronette, D.C.G., da Silva Torres, R., Borin, E. & Almeida, J. Effective, Efficient, and Scalable Unsupervised Distance Learning in Image Retrieval Tasks 2015 Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 51-58  inproceedings DOI URL 
    Abstract: Various unsupervised learning methods have been proposed with significant improvements in the effectiveness of image search systems. However, despite the relevant effectiveness gains, these approaches commonly require high computation efforts, not addressing properly efficiency and scalability requirements. In this paper, we present a novel unsupervised learning approach for improving the effectiveness of image retrieval tasks. The proposed method is also scalable and efficient as it exploits parallel and heterogeneous computing on CPU and GPU devices. Extensive experiments were conducted considering five different public image collections and several descriptors. This rigorous experimental protocol evaluates the effectiveness, efficiency, and scalability of the proposed approach, and compares it with previous methods. Experimental results demonstrate that high effectiveness gains (up to +29 can be obtained requiring small run times.
    BibTeX:
    @inproceedings{Valem2015ICMR,
      author = {Lucas Pascotti Valem and Daniel C. G. Pedronette and Ricardo da Silva Torres and Edson Borin and Jurandy Almeida},
      title = {Effective, Efficient, and Scalable Unsupervised Distance Learning in Image Retrieval Tasks},
      booktitle = {Proceedings of the 5th ACM on International Conference on Multimedia Retrieval},
      year = {2015},
      pages = {51--58},
      url = {http://doi.acm.org/10.1145/2671188.2749336},
      doi = {http://dx.doi.org/10.1145/2671188.2749336}
    }
    
    Waku, G.M., Bollis, E.R., Rubira, C.M.F. & da Silva Torres, R. A Robust Software Product Line Architecture for Data Collection in Android Platform 2015 IX Brazilian Symposium on Components, Architectures and Reuse Software (SBCARS), pp. 31-39  inproceedings DOI  
    Abstract: Android is an open platform, developed by Google and Open Mobile Handset Alliance targeting mobile devices. Its constant evolution and increasing cost reduction made them suitable for complex applications especially for data collection applications. Data collection is a domain which evolved to use mobile devices to collect information, targeting different fields of study including: physical and social sciences, humanities, business, demographic surveys, agriculture, biology, and geology. Android usually runs on different hardware and software domains with similar functional and non-functional features. The data collection domain has a lot of sub-domains, creating an opportunity to explore software variability and quality properties such as reliability, availability, and data integrity using Component-Based Development (CBD), Fault Tolerance Techniques and Software Product Line (SPL) with Aspect-Oriented Software Development (AOSD). However, the use of mobile application for data collection poses some challenges like severe hardware restrictions (such as limited power processing and short battery lifetime) and the use of sophisticated techniques can negatively impact in application performance, and quality properties. In this work, these issues were addressed by proposing the development of a robust SPL architecture called Robust SPL for Data Collection (R-SPL-DC) and a real application called E-Phenology Collector for data collection domain to assess the use fault tolerance techniques, CBD, SPL, and AOSD to ensure availability, reliability, and data integrity without significant impacts on the overall performance of the mobile device. The results have shown that the use of R-SPL-DC is promising and suits the requirements for data collection domain.
    BibTeX:
    @inproceedings{Waku2015SBCARS,
      author = {Gustavo M. Waku and Edson R. Bollis and Cecilia M. F. Rubira and Ricardo da Silva Torres},
      title = {A Robust Software Product Line Architecture for Data Collection in Android Platform},
      booktitle = {IX Brazilian Symposium on Components, Architectures and Reuse Software (SBCARS)},
      year = {2015},
      pages = {31-39},
      doi = {http://dx.doi.org/10.1109/SBCARS.2015.14}
    }
    
    Calumby, R.T., da Silva Torres, R. & calves, M.A.G. Diversity-driven Learning for Multimodal Image Retrieval with Relevance Feedback 2014 IEEE International Conference on Image Processing, pp. 2197-2201  inproceedings DOI  
    Abstract: We introduce a new genetic programming approach for enhancing the user search experience based on relevance feedback over results produced by a multimodal image retrieval technique with explicit diversity promotion. We have studied maximal marginal relevance re-ranking methods for result diversification and their impacts on the overall retrieval effectiveness. We show that the learning process using diverse results may improve user experience in terms of both the number of relevant items retrieved and subtopic coverage.
    BibTeX:
    @inproceedings{Calumby2014ICIP,
      author = {Rodrigo Tripodi Calumby and Ricardo da Silva Torres and Marcos A. Goncalves},
      title = {Diversity-driven Learning for Multimodal Image Retrieval with Relevance Feedback},
      booktitle = {IEEE International Conference on Image Processing},
      year = {2014},
      pages = {2197-2201},
      doi = {http://dx.doi.org/10.1109/ICIP.2014.7025445}
    }
    
    Conti, J., Faria, F.A., Almeida, J., Alberton, B., Morellato, L.P., Camolesi, L. & da Silva Torres, R. Evaluation of Time Series Distance Functions in the Task of Detecting Remote Phenology Patterns 2014 22nd International Conference on Pattern Recognition, pp. 3126-3131  inproceedings DOI  
    Abstract: Phenology is the study of periodic natural phenomena and their relationship to climate. Usually, phenology studies consider the identification of patterns on temporal data. In those studies, several phenological change patterns are often encoded in time series for analysis and knowledge extraction. In this paper, we evaluate the effectiveness of several time series similarity functions in the task of classifying time series related to phenological phenomena characterized by near-surface vegetation indices extracted from images. In addition, we performed a correlation analysis to identify potential candidates for combination.
    BibTeX:
    @inproceedings{Conti2014ICPR,
      author = {José Conti and Fabio A. Faria and Jurandy Almeida and Bruna Alberton and Leonor Patrícia Morellato and Luis Camolesi and Ricardo da Silva Torres},
      title = {Evaluation of Time Series Distance Functions in the Task of Detecting Remote Phenology Patterns},
      booktitle = {22nd International Conference on Pattern Recognition},
      year = {2014},
      pages = {3126-3131},
      doi = {http://dx.doi.org/10.1109/ICPR.2014.539}
    }
    
    Faria, F.A., Rocha, A. & da S., R. A Framework for Pattern Classifier Selection and Fusion 2014 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases  inproceedings  
    Abstract: In this work, we propose a framework for classifier selection and fusion. Our method seeks to combine image characterization and learning methods by means of a meta-learning approach responsible for assessing which methods contribute more towards the solution of a given problem. The framework uses three different strategies of classifier selection that pinpoint the less correlated, yet effective, classifiers through a series of diversity measure analysis. The experiments show that the proposed approaches yield comparable results to well-known algorithms from the literature on many different applications but using less learning and description methods as well as not incurring in the curse of dimensionality and normalization problems common to some fusion techniques. Furthermore, our approach yields effective classification results using very reduced training sets.
    BibTeX:
    @inproceedings{Faria2014ECML-PKDD,
      author = {Fabio A. Faria and Anderson Rocha and Ricardo da S.},
      title = {A Framework for Pattern Classifier Selection and Fusion},
      booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
      year = {2014}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Unsupervised Manifold Learning by Correlation Graph and Strongly Connected Components for Image Retrieval 2014 IEEE International Conference on Image Processing, pp. 1892-1896  inproceedings DOI  
    Abstract: This paper presents a novel manifold learning approach that takes into account the intrinsic dataset geometry. The dataset structure is modeled in terms of a Correlation Graph and analyzed using Strongly Connected Components (SCCs). The proposed manifold learning approach defines a more effective distance among images, used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach yields better results in terms of effectiveness than various methods recently proposed in the literature.
    BibTeX:
    @inproceedings{Pedronette2014ICIP,
      author = {Daniel C. G. Pedronette and Ricardo da S. Torres},
      title = {Unsupervised Manifold Learning by Correlation Graph and Strongly Connected Components for Image Retrieval},
      booktitle = {IEEE International Conference on Image Processing},
      year = {2014},
      pages = {1892-1896},
      doi = {http://dx.doi.org/10.1109/ICIP.2014.7025379}
    }
    
    Pedronette, D.C.G., Penatti, O.A.B., Calumby, R.T. & da Silva Torres, R. Unsupervised Distance Learning By Reciprocal kNN Distance for Image Retrieval 2014 Proceedings of International Conference on Multimedia Retrieval, pp. 345:345-345:352  inproceedings DOI URL 
    Abstract: This paper presents a novel unsupervised learning approach that takes into account the intrinsic dataset structure, which is represented in terms of the reciprocal neighborhood references found in different ranked lists. The proposed Reciprocal kNN Distance defines a more effective distance between two images, and is used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach is also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of proposed approach. The Reciprocal kNN Distance yields better results in terms of effectiveness than various state-of-the-art algorithms.
    BibTeX:
    @inproceedings{Pedronette2014ICMR,
      author = {Daniel C. G. Pedronette and Otávio A. B. Penatti and Rodrigo Tripodi Calumby and Ricardo da Silva Torres},
      title = {Unsupervised Distance Learning By Reciprocal kNN Distance for Image Retrieval},
      booktitle = {Proceedings of International Conference on Multimedia Retrieval},
      publisher = {ACM},
      year = {2014},
      pages = {345:345--345:352},
      url = {http://doi.acm.org/10.1145/2578726.2578770},
      doi = {http://dx.doi.org/10.1145/2578726.2578770}
    }
    
    Pedronette, D.C.G., Calumby, R.T. & da S. Torres, R. Semi-Supervised Learning for Relevance Feedback on Image Retrieval Tasks 2014 Conference on Graphics, Patterns and Images, pp. 243-250  inproceedings DOI  
    Abstract: Relevance feedback approaches have been estab- lished as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.
    BibTeX:
    @inproceedings{Pedronette2014SIBGRAPI,
      author = {Daniel C. G. Pedronette and Rodrigo T. Calumby and Ricardo da S. Torres},
      title = {Semi-Supervised Learning for Relevance Feedback on Image Retrieval Tasks},
      booktitle = {Conference on Graphics, Patterns and Images},
      year = {2014},
      pages = {243-250},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2014.44}
    }
    
    dos Santos, L.C.B., Almeida, J., Santos, J.A., Guimarães, S.J.G., de A. Araújo, A., Alberton, B., Morellato, L.P. & da Silva Torres, R. Phenological event detection by visual rhythm dissimilarity analysis 2014 10th IEEE International eScience Conference, pp. 263-270  inproceedings DOI  
    Abstract: Plant phenology has been exploited as an important research venue for assessing the impact of climate changes. One common approach for monitoring vegetation relies on the use of digital cameras. The employment of imaging techniques for phenological observation allows the extraction and analysis of visual characteristics based on color and texture information with the objective of determining plant life cycle changes, such as the beginning of the leaf flushing or the senescence period. This paper presents a novel approach for detecting phenological changes by analyzing image temporal series. Our method is based on the use of visual rhythm analysis and the adoption of a dissimilarity measure to detect visual changes in the time line. Experiments were conducted on a three-year data set composed of 3,538 vegetation images and 21 samples of 6 different species of interest. Results demonstrate that the proposed change detection approach is able to effectively identify phenological events.
    BibTeX:
    @inproceedings{Santos2014ESCIENCE,
      author = {Lilian C. B. dos Santos and Jurandy Almeida and Jefersson A. Santos and Silvio Jamil G. Guimarães and Arnaldo de A. Araújo and Bruna Alberton and Leonor Patrícia Morellato and Ricardo da Silva Torres},
      title = {Phenological event detection by visual rhythm dissimilarity analysis},
      booktitle = {10th IEEE International eScience Conference},
      year = {2014},
      pages = {263-270},
      doi = {http://dx.doi.org/10.1109/eScience.2014.23}
    }
    
    Silva, F.B., Tabbone, S. & da Silva Torres, R. BoG: a New Approach for Graph Matching 2014 22nd International Conference on Pattern Recognition, pp. 82-87  inproceedings DOI  
    Abstract: Huge volume of graph data are becoming available. This scenario demands the development of effective and efficient methods to perform graph matching. In this paper, we propose to adapt the Bag-of-Words model into the context of graphs. Using a vocabulary based on graph local structures, we represent graphs as histograms. Experiments show that our approach achieves good accuracy rates. Moreover, the advantage of this representation is that the computation of graph matching has a very low complexity, which allows to efficiently perform graph classification and retrieval on large datasets.
    BibTeX:
    @inproceedings{Silva2014ICPR,
      author = {Fernanda B. Silva and Salvatore Tabbone and Ricardo da Silva Torres},
      title = {BoG: a New Approach for Graph Matching},
      booktitle = {22nd International Conference on Pattern Recognition},
      year = {2014},
      pages = {82-87},
      doi = {http://dx.doi.org/10.1109/ICPR.2014.24}
    }
    
    Almeida, J., Santos, J.A., Alberton, B., Morellato, L.P. & da Silva Torres, R. Plant Species Identification with Phenological Visual Rhythms 2013 eScience (eScience), 2013 IEEE 9th International Conference on, pp. 148-154  inproceedings DOI  
    Abstract: Plant phenology studies recurrent plant life cycles events and is a key component of climate change research. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful are digital cameras, used as multi-channel imaging sensors to estimate color changes that are related to phenological events. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract individual plant color information and correlated with leaf phenological changes. To do so, time series associated with plant species were obtained, raising the need of using appropriate tools for mining patterns of interest. In this paper, we present a novel approach for representing phenological patterns of plant species derived from digital images. The proposed method is based on encoding time series as a visual rhythm, which is characterized by image description algorithms. A comparative analysis of different descriptors is conducted and discussed. Experimental results show that our approach presents high accuracy on identifying plant species.
    BibTeX:
    @inproceedings{Almeida2013ESCIENCE,
      author = {Jurandy Almeida and Jefersson A. Santos and Bruna Alberton and Leonor Patrícia Morellato and Ricardo da Silva Torres},
      title = {Plant Species Identification with Phenological Visual Rhythms},
      booktitle = {eScience (eScience), 2013 IEEE 9th International Conference on},
      year = {2013},
      pages = {148-154},
      doi = {http://dx.doi.org/10.1109/eScience.2013.43}
    }
    
    Almeida, J., Santos, J.A., Alberton, B., Morellato, L.P. & da S. Torres, R. Visual rhythm-based time series analysis for phenology studies 2013 Image Processing (ICIP), 2013 20th IEEE International Conference on, pp. 4412-4416  inproceedings DOI  
    Abstract: Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. In this context, digital cameras have been successfully used as multi-channel imaging sensors, providing measures to estimate changes on phenological events, such as leaf flushing and senescence. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. For that, we extract leaf color information and correlated with phenological changes. In this way, time series associated with plant species are obtained, raising the need of using appropriate tools for mining patterns of interest. In this paper, we present a novel approach for representing phenological patterns of plant species. The proposed method is based on encoding time series as a visual rhythm, which is characterized by color description algorithms. A comparative analysis of different descriptors is conducted and discussed. Experimental results show that our approach presents high accuracy on identifying plant species.
    BibTeX:
    @inproceedings{Almeida2013ICIP,
      author = {Jurandy Almeida and Jefersson A. Santos and Bruna Alberton and Leonor Patrícia Morellato and Ricardo da S. Torres},
      title = {Visual rhythm-based time series analysis for phenology studies},
      booktitle = {Image Processing (ICIP), 2013 20th IEEE International Conference on},
      year = {2013},
      pages = {4412-4416},
      doi = {http://dx.doi.org/10.1109/ICIP.2013.6738909}
    }
    
    Faria, F.A., Santos, J.A., Sarkar, S., Rocha, A. & da S. Torres, R. Classifier Selection Based on the Correlation of Diversity Measures: When Fewer Is More 2013 Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI - Conference on, pp. 16-23  inproceedings DOI  
    Abstract: The ever-growing access to high-resolution images has prompted the development of region-based classification methods for remote sensing images. However, in agricultural applications, the recognition of specific regions is still a challenge as there could be many different spectral patterns in a same studied area. In this context, depending on the features used, different learning methods can be used to create complementary classifiers. Many researchers have developed solutions based on the use of machine learning techniques to address these problems. Examples of successful initiatives are those dedicated to the development of learning techniques for data fusion or Multiple Classifier Systems (MCS). In MCS, diversity becomes an essential factor for their success. Different works have been using diversity measures to select appropriate high-performance classifiers, but the challenge of finding the optimal number of classifiers for a target task has not been properly addressed yet. In general, the proposed solutions rely on the a priori use of ad hoc strategies for selecting classifiers, followed by the evaluation of their effectiveness results during training. Searching by the optimal number of classifiers, however, makes the selection process more expensive. In this paper, we address this issue by proposing a novel strategy for selecting classifiers to be combined based on the correlation of different diversity measures. Diversity measures are used to rank pairs of classifiers and the agreement among ranked lists guides the classifier selection process. A fusion framework has been used in our experiments targeted to the classification of coffee crops in remote sensing images. Experiment results demonstrate that the novel strategy is able to yield comparable effectiveness results when contrasted to several baselines, but using much fewer classifiers.
    BibTeX:
    @inproceedings{Faria2013SIBGRAPI,
      author = {Fabio A. Faria and Jefersson A. Santos and Sudeep Sarkar and Anderson Rocha and Ricardo da S. Torres},
      title = {Classifier Selection Based on the Correlation of Diversity Measures: When Fewer Is More},
      booktitle = {Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI - Conference on},
      year = {2013},
      pages = {16-23},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2013.12}
    }
    
    Godoi, T.A., da Silva Torres, R., Carvalho, A.M., calves, M.A.G., Ferreira, A.A., Fan, W. & Fox, E.A. A Relevance Feedback Approach for the Author Name Disambiguation Problem 2013 Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 209-218  inproceedings DOI URL 
    Abstract: This paper presents a new name disambiguation method that exploits user feedback on ambiguous references across iterations. An unsupervised step is used to define pure training samples, and a hybrid supervised step is employed to learn a classification model for assigning references to authors. Our classification scheme combines the Optimum-Path Forest (OPF) classifier with complex reference similarity functions generated by a Genetic Programming framework. Experiments demonstrate that the proposed method yields better results than state-of-the-art disambiguation methods on two traditional datasets.
    BibTeX:
    @inproceedings{Godoi2013JCDL,
      author = {Thiago A. Godoi and Ricardo da Silva Torres and Ariadne M.B.R. Carvalho and Marcos A. Goncalves and Anderson A. Ferreira and Weiguo Fan and Edward A. Fox},
      title = {A Relevance Feedback Approach for the Author Name Disambiguation Problem},
      booktitle = {Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries},
      publisher = {ACM},
      year = {2013},
      pages = {209--218},
      url = {http://doi.acm.org/10.1145/2467696.2467709},
      doi = {http://dx.doi.org/10.1145/2467696.2467709}
    }
    
    Muraro, E., Mariano, G., Kozievitch, N.P., Almeida, J., Santos, J.A., da S. Torres, R., Alberton, B. & Morellato, L.P. A Framework for Semantic Annotation of Phenology Image Components 2013 International Conference on Digital Libraries, pp. 243-256  inproceedings  
    Abstract: Due to the dissemination of low-cost devices for acquisition, storage, and sharing, images have been used in several e-science applications. The use of images in those applications has motivated the creation of heterogeneous digital objects. In fact, images are not longer used in isolation and are used to compose other digital objects, named Complex Objects. In this work, we present a new framework for automatic semantic annotation of Phenology image components, aiming at supporting their use in the construction of complex objects. We propose the application of several approaches for defining appropriate terms to be used in the annotation process of daily series of Phenology digital images of plant crowns: ontologies, textual terms, and image content descriptions. The main contributions of this work are: (i) the specification of an automatic semantic annotation process for image components, that takes into account image visual descriptors, defined textual terms, ontologies, and their combination; and (ii) the specification and partial implementation of an infrastructure for annotating image complex objects in the context of Phenology studies.
    BibTeX:
    @inproceedings{Muraro2013ICDL,
      author = {Emerson Muraro and Greice Mariano and Nádia P. Kozievitch and Jurandy Almeida and Jefersson A. Santos and Ricardo da S. Torres and Bruna Alberton and Leonor Patrícia Morellato},
      title = {A Framework for Semantic Annotation of Phenology Image Components},
      booktitle = {International Conference on Digital Libraries},
      year = {2013},
      pages = {243-256}
    }
    
    Pedronette, D.C.G., da Silva Torres, R., Borin, E. & Jr., M.B. Image Re-ranking Acceleration on GPUs 2013 25th International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2013, Porto de Galinhas, Pernambuco, Brazil, October 23-26, 2013, pp. 176-183  inproceedings DOI URL 
    Abstract: Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.
    BibTeX:
    @inproceedings{Pedronette2013SBAC-PAD,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da Silva Torres and Edson Borin and Mauricio Breternitz Jr.},
      title = {Image Re-ranking Acceleration on GPUs},
      booktitle = {25th International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2013, Porto de Galinhas, Pernambuco, Brazil, October 23-26, 2013},
      year = {2013},
      pages = {176--183},
      url = {http://dx.doi.org/10.1109/SBAC-PAD.2013.19},
      doi = {http://dx.doi.org/10.1109/SBAC-PAD.2013.19}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Unsupervised Measures for Estimating the Effectiveness of Image Retrieval Systems 2013 Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI - Conference on, pp. 341-348  inproceedings DOI  
    Abstract: The main objective of Content-Based Image Retrieval (CBIR) systems is to retrieve a ranked list containing the most similar images of a collection given a query image, by taking into account their visual content. Although these systems represent a very promising approach, in many situations is very challenging to assure the quality of returned ranked lists. Supervised approaches rely on training data and information obtained from user interactions to identify and then improve low-quality results. However, these approaches require a lot of human efforts which can be infeasible for many systems. In this paper, we present two novel unsupervised measures for estimating the effectiveness of ranked lists in CBIR tasks. Given an estimation of the effectiveness of ranked lists, many CBIR systems can, for example, emulate the training process, but now without any user intervention. Improvements can also be achieved on several unsupervised approaches, such as re-ranking and rank aggregation methods, once the estimation measures can help to consider more relevant information by distinguishing effective from non-effective ranked lists. Both proposed measures are computed using a novel image representation of ranked lists and distances among images considering a given dataset. The objective is to exploit the visual patterns encoded in the image representations for estimating the effectiveness of ranked lists. Experiments involving shape, color, and texture descriptors demonstrate that the proposed approaches can provide accurate estimations of the quality in terms of effectiveness of ranked lists. The use of proposed measures are also evaluated in image retrieval tasks aiming at improving the effectiveness of rank aggregation approaches.
    BibTeX:
    @inproceedings{Pedronette2013SIBGRAPI,
      author = {Daniel C. G. Pedronette and Ricardo da S. Torres},
      title = {Unsupervised Measures for Estimating the Effectiveness of Image Retrieval Systems},
      booktitle = {Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI - Conference on},
      year = {2013},
      pages = {341-348},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2013.54}
    }
    
    Pereira, L.A.M., Papa, J.P., Almeida, J., da S. Torres, R. & Paraguassu, W.A. A Multiple Labeling-Based Optimum-Path Forest for Video Content Classification 2013 Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI - Conference on, pp. 334-340  inproceedings DOI  
    Abstract: Multiple-labeling classification approaches attempt to handle applications that associate more than one label to a given sample. Since we have an increasing number of systems that are guided by such assumption, in this paper we have presented a multiple-labeling approach for the Optimum-Path Forest (OPF) classifier based on the problem transformation method. In order to validate our proposal, a multi-labeled video classification dataset has been used to compare OPF against three other classifiers and another variant of the OPF classifier based on a k-neighborhood. The results have shown the validity of the OPF-based classifiers for multi-labeling classification problems.
    BibTeX:
    @inproceedings{Pereira2013SIBGRAPI,
      author = {Luis A. M. Pereira and João Paulo Papa and Jurandy Almeida and Ricardo da S. Torres and Willian Amorim Paraguassu},
      title = {A Multiple Labeling-Based Optimum-Path Forest for Video Content Classification},
      booktitle = {Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI - Conference on},
      year = {2013},
      pages = {334-340},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2013.53}
    }
    
    Santos, J.A., Penatti, O.A.B., da Silva Torres, R., Gosselin, P.-H., Philipp-Foliguet, S. & Falcão, A.X. Remote sensing image representation based on hierarchical histogram propagation 2013 Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, pp. 2982-2985  inproceedings DOI  
    Abstract: Many methods have been recently proposed to deal with the large amount of data provided by high-resolution remote sensing technologies. Several of these methods rely on the use of image segmentation algorithms for delineating target objects. However, a common issue in geographic object-based applications is the definition of the appropriate data representation scale, a problem that can be addressed by exploiting multiscale segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting histogram-based features from a hierarchy of regions for multiscale classification. The strategy, called H-Propagation, exploits the existing relationships among regions in a hierarchy to iteratively propagate features along multiple scales. The proposed method speeds up the feature extraction process and yields good results when compared with global low-level extraction approaches.
    BibTeX:
    @inproceedings{Santos2013IGARSS,
      author = {Jefersson A. Santos and Otávio A. B. Penatti and Ricardo da Silva Torres and Gosselin, P.-H. and Philipp-Foliguet, S. and Alexandre X. Falcão},
      title = {Remote sensing image representation based on hierarchical histogram propagation},
      booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International},
      year = {2013},
      pages = {2982-2985},
      doi = {http://dx.doi.org/10.1109/IGARSS.2013.6723452}
    }
    
    Silva, F.B., Goldenstein, S., Tabbone, S. & da Silva Torres, R. Image classification based on bag of visual graphs 2013 Image Processing (ICIP), 2013 20th IEEE International Conference on, pp. 4312-4316  inproceedings DOI  
    Abstract: This paper proposes the Bag of Visual Graphs (BoVG), a new approach to encode the spatial relationships of visual words through a codebook of visual-word arrangements, represented by graphs. This graph-based codebook defines a descriptor for image representations that not only considers the frequency of occurrence of visual words, but also their spatial relationships. Experiments demonstrate that BoVG yields high-accuracy scores in classification tasks on the traditional Caltech-101 and Caltech-256 datasets.
    BibTeX:
    @inproceedings{Silva2013ICIP,
      author = {Fernanda B. Silva and Siome Goldenstein and Salvatore Tabbone and Ricardo da Silva Torres},
      title = {Image classification based on bag of visual graphs},
      booktitle = {Image Processing (ICIP), 2013 20th IEEE International Conference on},
      year = {2013},
      pages = {4312-4316},
      doi = {http://dx.doi.org/10.1109/ICIP.2013.6738888}
    }
    
    Toffoli, T.O., Kozievitch, N.P., Gonçalves, M.A. & Torres, R. d.S. A Formal Approach for the Specification of Digital Complex Objects 2013 Proceedings of the 19th Brazilian Symposium on Multimedia and the Web, pp. 125-132  inproceedings DOI URL 
    Abstract: Complex objects (COs) have surged as a way to integrate different digital resources under a same logical unit in order to facilitate aggregation and reuse. However, there is still a lack of consensus on precise theoretical foundations for COs, especially regarding design and specification, which compromise their utility and integration with existing software tools. Moreover, there has been little investigation on aspects related to the modeling of COs by the end user, much due to the lack of appropriate tools for this goal. In this work, we present a new Digital Library (DL) metamodel specially designed for the CO modeling which is grounded in formal theoretical specification for COs. More specifically, our goal is two-fold: (i) to indirectly validate our CO formalization by instantiating it within a DL modeling tool -- 5SGraph; and (ii) to investigate the difficulties of CO modeling and specification by real users using the specified metamodel. Experiments with real users indicate that the use of the metamodel and the graphical tool facilitates the understanding of the COs structure and the modeling process.
    BibTeX:
    @inproceedings{Toffoli2013Webmedia,
      author = {Toffoli, Ticiana Oniki and Kozievitch, Nádia Puchalski and Gonçalves, Marcos André and Torres, Ricardo da Silva},
      title = {A Formal Approach for the Specification of Digital Complex Objects},
      booktitle = {Proceedings of the 19th Brazilian Symposium on Multimedia and the Web},
      publisher = {ACM},
      year = {2013},
      pages = {125--132},
      url = {http://doi.acm.org/10.1145/2526188.2526199},
      doi = {http://dx.doi.org/10.1145/2526188.2526199}
    }
    
    da S. Torres, R., Hasegawa, M., Tabbone, S., Almeida, J., Santos, J.A., Alberton, B. & Morellato, L.P. Shape-based time series analysis for remote phenology studies 2013 Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, pp. 3598-3601  inproceedings DOI  
    Abstract: Remote phenology has motivated the development of new technologies for pattern observation. In this scenario, digital cameras have been used as data source for studies that estimate changes on phenological events. In this paper, we investigate the use of shape descriptors in the task of characterizing time series associated with phenological changes. The main objectives are: i) to determine which color channel is better for extracting shape descriptors and ii) to analyze the impact of the sunshine on the performance of shape descriptors.
    BibTeX:
    @inproceedings{Torres2013IGARSS,
      author = {Ricardo da S. Torres and Makoto Hasegawa and Salvatore Tabbone and Jurandy Almeida and Jefersson A. Santos and Bruna Alberton and Leonor Patrícia Morellato},
      title = {Shape-based time series analysis for remote phenology studies},
      booktitle = {Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International},
      year = {2013},
      pages = {3598-3601},
      doi = {http://dx.doi.org/10.1109/IGARSS.2013.6723608}
    }
    
    Almeida, J., dos Santos, J.A., Alberton, B., da S. Torres, R. & Morellato, L.P. Remote Phenology: Applying Machine Learning to Detect Phenological Patterns in a Cerrado Savanna 2012 8th IEEE International Conference on E-Science, pp. -  inproceedings DOI  
    Abstract: Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground.
    BibTeX:
    @inproceedings{Almeida2012ESCIENCE,
      author = {Jurandy Almeida and Jefersson A. dos Santos and Bruna Alberton and Ricardo da S. Torres and Leonor Patrícia Morellato},
      title = {Remote Phenology: Applying Machine Learning to Detect Phenological Patterns in a Cerrado Savanna},
      booktitle = {8th IEEE International Conference on E-Science},
      year = {2012},
      pages = {--},
      doi = {http://dx.doi.org/10.1109/eScience.2012.6404438}
    }
    
    Andrade, F.S.P., Almeida, J., Pedrini, Hé. & da S. Torres, R. Fusion of Local and Global Descriptors for Content-Based Image and Video Retrieval 2012 17th Iberoamerican Congress on Pattern Recognition, pp. 845-853  inproceedings DOI  
    Abstract: Recently, fusion of descriptors has become a trend for improving the performance in image and video retrieval tasks. Descriptors can be global or local, depending on how they analyze visual content. Most of existing works have focused on the fusion of a single type of descriptor. Different from all of them, this paper aims to analyze the impact of combining global and local descriptors. Here, we perform a comparative study of different types of descriptors and all of their possible combinations. Extensive experiments of a rigorous experimental design show that global and local descriptors complement each other, such that, when combined, they outperform other combinations or single descriptors.
    BibTeX:
    @inproceedings{Andrade2012CIARP,
      author = {Felipe S. P. Andrade and Jurandy Almeida and Hélio Pedrini and Ricardo da S. Torres},
      title = {Fusion of Local and Global Descriptors for Content-Based Image and Video Retrieval},
      booktitle = {17th Iberoamerican Congress on Pattern Recognition},
      year = {2012},
      pages = {845-853},
      doi = {http://dx.doi.org/10.1007/978-3-642-33275-3\_104}
    }
    
    Bueno, L.M., Valle, E. & da S. Torres, R. Bayesian approach for near-duplicate image detection 2012 International Conference on Multimedia Retrieval, (ICMR), pp. 15  inproceedings DOI  
    Abstract: Vote-based algorithms are very popular in tasks based on image local-descriptors, including object matching, panoramic stitching and near-duplicate detection. On this paper, we focus on the latter application, proposing a Bayesian approach, which allows giving a probabilistic interpretation to the distances between local descriptors in the feature space. That contrasts with traditional schemes, in which the distances are used to establish a simple unweighted vote count. Near-duplicate detection is demanded for a myriad of applications: metadata retrieval in cultural institutions, detection of copyright violations, duplicate elimination in storage, etc. The majority of current solutions are based either on voting algorithms, which are very precise, but expensive; or on the use of visual dictionaries, which are efficient, but less precise. Contrarily to raw-vote based systems, our scheme performs few database accesses; and contrarily to dictionary-based systems, it allows a fine control of the compromise between precision and efficiency. In our experiments, it yields 99% accuracy with less than 10 database accesses, in contrast with the hundreds needed in raw-voting schemes.
    BibTeX:
    @inproceedings{Bueno2012ICMR,
      author = {Lucas Moutinho Bueno and Eduardo Valle and Ricardo da S. Torres},
      title = {Bayesian approach for near-duplicate image detection},
      booktitle = {International Conference on Multimedia Retrieval, (ICMR)},
      year = {2012},
      pages = {15},
      doi = {http://dx.doi.org/10.1145/2324796.2324815}
    }
    
    Faria, F.A., dos Santos, J.A., da S. Torres, R., Rocha, A. & Falcão, A.X. Automatic fusion of region-based classifiers for coffee crop recognition 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2221-2224  inproceedings DOI  
    Abstract: Coffee crop recognition in remote sensing images is a complex task. It poses several challenges due to different spectral responses and texture patterns that can be extracted from coffee regions. This paper presents a novel framework for combining different classifiers using support vector machine technique (SVM), which try to learn with each one of classifiers previews experiences (meta-learning). We investigate the combination of seven learning methods and seven image descriptors aiming at creating low-cost classifiers for coffee crops recognition. The objective is to provide an effective mechanism for coffee crop recognition by fusion of region-based classifiers in remote sensing images. The experiments showed that the proposed framework for fusion of classifiers produces better results than the traditional majority voting fusion approach and all base classifiers tested.
    BibTeX:
    @inproceedings{Faria2012IGARSS,
      author = {Fabio A. Faria and Jefersson Alex dos Santos and Ricardo da S. Torres and Anderson Rocha and Alexandre X. Falcão},
      title = {Automatic fusion of region-based classifiers for coffee crop recognition},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      year = {2012},
      pages = {2221-2224},
      doi = {http://dx.doi.org/10.1109/IGARSS.2012.6351058}
    }
    
    Faria, F.A., dos Santos, J.A., Rocha, A. & da S. Torres, R. Automatic Classifier Fusion for Produce Recognition 2012 Conference on Graphics, Patterns and Images (25th SIBGRAPI), pp. 252-259  inproceedings DOI  
    Abstract: Recognizing different kinds of fruits and vegetables is a common task in supermarkets. This task, however, poses several challenges as it requires the identification of different species of a particular produce and also its variety. Usually, existing computer-based recognition approaches are not automatic and demand long-term and laborious prior training sessions. This paper presents a novel framework for classifier fusion aiming at supporting the automatic recognition of fruits and vegetables in a supermarket environment. The objective is to provide an effective mechanism for combining low-cost classifiers trained for specific classes of interest. The experiments performed demonstrate that the proposed framework yields better results than several related work found in the literature and represents a step forward automatic produce recognition in cashiers of supermarkets.
    BibTeX:
    @inproceedings{Faria2012SIBGRAPI,
      author = {Fabio A. Faria and Jefersson A. dos Santos and Anderson Rocha and Ricardo da S. Torres},
      title = {Automatic Classifier Fusion for Produce Recognition},
      booktitle = {Conference on Graphics, Patterns and Images (25th SIBGRAPI)},
      year = {2012},
      pages = {252-259},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2012.42}
    }
    
    Mansano, A.F., Matsuoka, J.A., Afonso, L.C.S., Faria, F.A. & da S. Torres, R. Improving Image Classification Through Descriptor Combination 2012 Conference on Graphics, Patterns and Images (25th SIBGRAPI), pp. 324-329  inproceedings DOI  
    Abstract: The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets.
    BibTeX:
    @inproceedings{Mansano2012SIBGRAPI,
      author = {Alex Fernandes Mansano and Jéssica Akemi Matsuoka and Luis Cláudio Súgi Afonso and Fabio A. Faria and Ricardo da S. Torres},
      title = {Improving Image Classification Through Descriptor Combination},
      booktitle = {Conference on Graphics, Patterns and Images (25th SIBGRAPI)},
      year = {2012},
      pages = {324-329},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2012.52}
    }
    
    Mariano, G., Almeida, J., da S. Torres, R., Alberton, B. & Morellato, L.P. Desenvolvimento de um Modelo Conceitual de um Banco de Dados para o Projeto e-phenology 2012 VI e-Science Workshop, XXXII Congresso da Sociedade Brasileira de Computacão (CSBC), pp. -  inproceedings  
    Abstract: Recently, phenology has gained importance as the more simple and reliable indicator of effects of climate changes on plants and animals. In this context, we highlight the e-phenology, a multidisciplinary project combining research in computer science and phenology, in order to attack theoretical and practical problems involving the use of new technologies for remote phenological observation. From the computer science point of view, it is necessary to develop models, tools and techniques concerning the storage, mining, processing, and retrieving of information of phenological changes. In this sense, this paper presents our achievements regarding the modeling and implementation of a database to manage information handled in the e-phenology project.
    BibTeX:
    @inproceedings{Mariano2012ESCIENCE-SBC,
      author = {Greice Mariano and Jurandy Almeida and Ricardo da S. Torres and Bruna Alberton and Leonor Patrícia Morellato},
      title = {Desenvolvimento de um Modelo Conceitual de um Banco de Dados para o Projeto e-phenology},
      booktitle = {VI e-Science Workshop, XXXII Congresso da Sociedade Brasileira de Computacão (CSBC)},
      year = {2012},
      pages = {--}
    }
    
    Nakamura, R., ao Paulo Papa, J., Fonseca, L.M., dos Santos, J.A. & da S. Torres, R. Hyperspectral Band Selection Through Optimum-Path Forest and Evolutionary-Based Algorithms 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3066-3069  inproceedings DOI  
    Abstract: In this paper we addressed the problem of dimensionality reduction in hyperspectral imagery classification by combining OPF classifier together with three recent evolutionary-based optimization algorithms: PSO, HS and GSA. We conducted experiments with two public datasets (Indian Pines and Salinas), which demonstrated that OPF combined with HS and GSA have obtained promising results, being the former the fastest approach. In regard to Indian Pines dataset, HS and GSA have achieved close classification rates, but HS has selected 46.25% less bands, which means a faster feature extraction step. For future works, we intend to provide a more detailed convergence analysis for PSO, HS and GSA, and also to introduce novel evolutionary-based band selection techniques and also to apply these methodologies for hyperspectral image classification in forest and agriculture applications.
    BibTeX:
    @inproceedings{Nakamura2012IGARSS,
      author = {Rodrigo Nakamura and João Paulo Papa and Leila M. Fonseca and Jefersson A. dos Santos and Ricardo da S. Torres},
      title = {Hyperspectral Band Selection Through Optimum-Path Forest and Evolutionary-Based Algorithms},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      year = {2012},
      pages = {3066-3069},
      doi = {http://dx.doi.org/10.1109/IGARSS.2012.6350778}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Combining Re-Ranking and Rank Aggregation Methods 2012 17th Iberoamerican Congress on Pattern Recognition, pp. 170-178  inproceedings DOI  
    Abstract: Content-Based Image Retrieval (CBIR) aims at retrieving the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches produce different image rankings. These rankings are complementary and, therefore, can be further combined aiming at obtaining more effective results. This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of CBIR systems. Several experiments were conducted involving shape, color, and texture descriptors. Experimental results demonstrate that our approaches can improve the effectiveness of CBIR systems.
    BibTeX:
    @inproceedings{Pedronette2012CIARP,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da S. Torres},
      title = {Combining Re-Ranking and Rank Aggregation Methods},
      booktitle = {17th Iberoamerican Congress on Pattern Recognition},
      year = {2012},
      pages = {170-178},
      doi = {http://dx.doi.org/10.1007/978-3-642-33275-3\_21}
    }
    
    Pedronette, D.C.G., da Silva Torres, R., Borin, E. & Jr., M.B. Efficient Image Re-Ranking Computation on GPUs 2012 10th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), pp. 95-102  inproceedings DOI  
    Abstract: The huge growth of image collections and multimedia resources available is remarkable. One of the most common approaches to support image searches relies on the use of Content-Based Image Retrieval (CBIR) systems. CBIR systems aim at retrieving the most similar images in a collection, given a query image. Since the effectiveness of those systems is very dependent on the accuracy of ranking approaches, re-ranking algorithms have been proposed to exploit contextual information and improve the effectiveness of CBIR systems. Image re-ranking algorithms typically consider the relationship among every image in a given dataset when computing the new ranking. This approach demands a huge amount of computational power, which may render it prohibitive on very large data sets. In order to mitigate this problem, we propose using the computational power of Graphics Processing Units (GPU) to speedup the computation of image re-ranking algorithms. GPUs are fast emerging and relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. In this paper, we propose a parallel implementation of an image re-ranking algorithm designed to fit the computational model of GPUs. Experimental results demonstrate that relevant performance gains can be obtained by our approach.
    BibTeX:
    @inproceedings{Pedronette2012ISPA,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da Silva Torres and Edson Borin and Mauricio Breternitz Jr.},
      title = {Efficient Image Re-Ranking Computation on GPUs},
      booktitle = {10th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)},
      year = {2012},
      pages = {95-102},
      doi = {http://dx.doi.org/10.1109/ISPA.2012.21}
    }
    
    Penatti, O.A.B., Li, L.T., Almeida, J. & da S. Torres, R. A visual approach for video geocoding using bag-of-scenes 2012 International Conference on Multimedia Retrieval, (ICMR), pp. 53  inproceedings DOI  
    Abstract: This paper presents a novel approach for video representation, called bag-of-scenes. The proposed method is based on dictionaries of scenes, which provide a high-level representation for videos. Scenes are elements with much more semantic information than local features, specially for geotagging videos using visual content. Thus, each component of the representation model has self-contained semantics and, hence, it can be directly related to a specific place of interest. Experiments were conducted in the context of the MediaEval 2011 Placing Task. The reported results show our strategy compared to those from other participants that used only visual content to accomplish this task. Despite our very simple way to generate the visual dictionary, which has taken photos at random, the results show that our approach presents high accuracy relative to the state-of-the art solutions.
    BibTeX:
    @inproceedings{Penatti2012ICMR,
      author = {Otávio Augusto Bizetto Penatti and Lin Tzy Li and Jurandy Almeida and Ricardo da S. Torres},
      title = {A visual approach for video geocoding using bag-of-scenes},
      booktitle = {International Conference on Multimedia Retrieval, (ICMR)},
      year = {2012},
      pages = {53},
      doi = {http://dx.doi.org/10.1145/2324796.2324857}
    }
    
    dos Santos, J.A., Faria, F., da S. Torres, R., Gosselin, P.-H., Philipp-Foliguet, S., Falcão, A.X. & Rocha, A. Descriptor Correlation Analysis for Remote Sensing Image Multi-Scale Classification 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3078-3081  inproceedings  
    Abstract: This paper addresses the problem of remote sensing image multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically select segmentation scales, descriptors, and classifiers based on correlation and accuracy analysis.
    BibTeX:
    @inproceedings{Santos2012ICPR-1,
      author = {Jefersson Alex dos Santos and Fabio Faria and Ricardo da S. Torres and Philippe-Henri Gosselin and Sylvie Philipp-Foliguet and Alexandre X. Falcão and Anderson Rocha},
      title = {Descriptor Correlation Analysis for Remote Sensing Image Multi-Scale Classification},
      booktitle = {21st International Conference on Pattern Recognition (ICPR)},
      year = {2012},
      pages = {3078-3081}
    }
    
    dos Santos, J.A., Penatti, O.A., da S. Torres, R., Gosselin, P.-H., Philipp-Foliguet, S. & Falcão, A.X. Improving Texture Description in Remote Sensing Image Multi-Scale Classification Tasks By Using Visual Words 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3090-3093  inproceedings  
    Abstract: Although texture features are important for region-based classification of remote sensing images, the literature shows that texture descriptors usually have poor performance when compared and combined with color descriptors. In this paper, we propose a bag-of-visual-words (BOW) ``propagation" approach to extract texture features from a hierarchy of regions. This strategy improves efficacy of feature as it encodes texture information independently of the region shape. Experiments show that the proposed approach improves the classification results when compared with global descriptors using the bounding box padding strategy.
    BibTeX:
    @inproceedings{Santos2012ICPR-2,
      author = {Jefersson Alex dos Santos and Otávio A.B. Penatti and Ricardo da S. Torres and Philippe-Henri Gosselin and Sylvie Philipp-Foliguet and Alexandre X. Falcão},
      title = {Improving Texture Description in Remote Sensing Image Multi-Scale Classification Tasks By Using Visual Words},
      booktitle = {21st International Conference on Pattern Recognition (ICPR)},
      year = {2012},
      pages = {3090-3093}
    }
    
    Vidal, M., ao M.B. Cvalcanti, J., Moura, E.S., Silva, A.S. & da S. Torres, R. Sorted Dominant Local Color for Searching Large and Heterogeneous Image Databases 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1960-1963  inproceedings  
    Abstract: Recent work on Content-Based Image Retrieval (CBIR) have presented alternative methods for fast image indexing and retrieval using Bags of Visual Words (BoVW). In such methods, images are represented as sets of visual words, which can be indexed and searched using well-known text retrieval techniques, allowing fast search on large image databases. In this paper we propose a novel method based on BoVW that improves over current methods by using a new kind of local color descriptor, which we call SDLC, that encodes the most predominant color occurrences in blocks of different image regions. We report results of experiments we performed with two publicly available image databases. The results indicate that the use of SDLC led to a quite competitive CBIR method in comparison to the state-of-the-art.
    BibTeX:
    @inproceedings{Vidal2012ICPR,
      author = {Márcio Vidal and João M. B. Cvalcanti and Edleno S. Moura and Altigran S. Silva and Ricardo da S. Torres},
      title = {Sorted Dominant Local Color for Searching Large and Heterogeneous Image Databases},
      booktitle = {21st International Conference on Pattern Recognition (ICPR)},
      year = {2012},
      pages = {1960-1963}
    }
    
    Almeida, J., Leite, N.J. & da S. Torres, R. Rapid Cut Detection on Compressed Video 2011 16th Iberoamerican Congress (CIARP), pp. 71-78  inproceedings DOI  
    Abstract: The temporal segmentation of a video sequence is one of the most important aspects for video processing, analysis, indexing, and retrieval. Most of existing techniques to address the problem of identifying the boundary between consecutive shots have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are two extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for video cut detection that works in the compressed domain. The proposed method is based on both exploiting visual features extracted from the video stream and on using a simple and fast algorithm to detect the video transitions. Experiments on a real-world video dataset with several genres show that our approach presents high accuracy relative to the state-of-the-art solutions and in a computational time that makes it suitable for online usage.
    BibTeX:
    @inproceedings{Almeida2011CIARP,
      author = {Jurandy Almeida and Neucimar J. Leite and Ricardo da S. Torres},
      title = {Rapid Cut Detection on Compressed Video},
      booktitle = {16th Iberoamerican Congress (CIARP)},
      year = {2011},
      pages = {71-78},
      doi = {http://dx.doi.org/10.1007/978-3-642-25085-9\_8}
    }
    
    Almeida, J., Leite, N.A. & da S. Torres, R. Comparison of Video Sequences with Histograms of Motion Patterns 2011 IEEE International Conference on Image Processing, pp. 3673-3676  inproceedings DOI  
    Abstract: Making efficient use of video information requires the development of a video signature and a similarity measure to rapidly identify similar videos in a huge database. Most of existing techniques to address this problem have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for comparing video sequences that works in the compressed domain. The proposed method is based on recognizing motion patterns extracted from the video stream and their occurrence histogram is proven to be a powerful feature for describing the video content. Experiments on a TRECVID 2010 dataset show that our approach presents high accuracy relative to the state-of- the-art solutions and in a computational time that makes it suitable for large collections.
    BibTeX:
    @inproceedings{Almeida2011ICIP,
      author = {Jurandy Almeida and Neucimar A. Leite and Ricardo da S. Torres},
      title = {Comparison of Video Sequences with Histograms of Motion Patterns},
      booktitle = {IEEE International Conference on Image Processing},
      year = {2011},
      pages = {3673-3676},
      doi = {http://dx.doi.org/10.1109/ICIP.2011.6116516}
    }
    
    Kozievitch, Ná.P., da S. Torres, R., Santanchè, A. & Leite, N.J. Reusing a compound-based infrastructure for searching video stories 2011 IEEE International Conference on Information Reuse and Integration (IRI), pp. 222-227  inproceedings DOI  
    Abstract: The fast evolution of technology has led to a growing demand for multimedia data, increasing the amount of research into efficient systems to manage those materials. A lot of research has being done by the Content-Based Image Retrieval (CBIR) community in the field of images. Nowadays, they play a key role in digital applications. Thus, contextual integration of images with different sources is vital. It involves reusing and aggregating a large amount of information with other media types. In particular, if we consider video data, images can be used to summarize videos into storyboards, providing an easy way to navigate and to browse large video collections. This has been the goal of a quickly evolving research area known as video summarization. In this paper, we present a novel approach to reuse the CBIR infrastructure for searching video stories, taking advantage of the compound object (CO) concept to integrate resources. Our approach relies on a specific component technology to encapsulate the CBIR related tasks and integrate them with video summarization techniques, known as Digital Content Component (DCC). Such a strategy provides an effective way to reuse, compose, and aggregate both content and processing software.
    BibTeX:
    @inproceedings{Kozievitch2011IRI,
      author = {Nádia P. Kozievitch and Ricardo da S. Torres and André Santanchè and Neucimar J. Leite},
      title = {Reusing a compound-based infrastructure for searching video stories},
      booktitle = {IEEE International Conference on Information Reuse and Integration (IRI)},
      year = {2011},
      pages = {222--227},
      doi = {http://dx.doi.org/10.1109/IRI.2011.6009550}
    }
    
    24th SIBGRAPI Conference on Graphics, Patterns and Images, Sibgrapi 2011, Alagoas, Maceió, Brazil, August 28-31, 2011 2011   proceedings URL 
    BibTeX:
    @proceedings{Lewiner2011SIBGRAPI,,
      title = {24th SIBGRAPI Conference on Graphics, Patterns and Images, Sibgrapi 2011, Alagoas, Maceió, Brazil, August 28-31, 2011},
      publisher = {IEEE},
      year = {2011},
      url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6134712}
    }
    
    Murthy, U., Li, L.T., Hallerman, E., Fox, E.A., Pérez-Quiñones, M.A., Delcambre, L.M.L. & da S. Torres, R. Use of subimages in fish species identification: a qualitative study 2011 Joint International Conference on Digital Libraries (JCDL), pp. 185-194  inproceedings DOI  
    Abstract: Many scholarly tasks involve working with subdocuments, or contextualized fine-grain information, i.e., with information that is part of some larger unit. A digital library (DL) facilitates management, access, retrieval, and use of collections of data and metadata through services. However, most DLs do not provide infrastructure or services to support working with subdocuments. Superimposed information (SI) refers to new information that is created to reference subdocuments in existing information resources. We combine this idea of SI with traditional DL services, to define and develop a DL with SI (SI-DL). We explored the use of subimages and evaluated the use of SuperIDR, a prototype SI-DL, in fish species identification, a scholarly task that involves working with subimages. The contexts and strategies of working with subimages in SuperIDR suggest new and enhanced support (SI-DL services) for scholarly tasks that involve working with subimages, including new ways of querying and searching for subimages and associated information. The main conceptual contributions of our work are the insights gained from these findings of the use of subimages and of SuperIDR, which lead to recommendations for the design of digital libraries with superimposed information.
    BibTeX:
    @inproceedings{Murthy2011JCDL,
      author = {Uma Murthy and Lin Tzy Li and Eric Hallerman and Edward A. Fox and Manuel A. Pérez-Quiñones and Lois M. L. Delcambre and Ricardo da S. Torres},
      title = {Use of subimages in fish species identification: a qualitative study},
      booktitle = {Joint International Conference on Digital Libraries (JCDL)},
      year = {2011},
      pages = {185--194},
      doi = {http://dx.doi.org/10.1145/1998076.1998112}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Image Re-ranking and Rank Aggregation Based on Similarity of Ranked Lists 2011 14th International Conference on Computer Analysis of Images and Patterns (CAIP), pp. 369-376  inproceedings DOI  
    Abstract: The objective of Content-based Image Retrieval (CBIR) systems is to return a ranked list containing the most similar images in a collection given a query image. The effectiveness of these systems is very dependent on the accuracy of the distance function adopted. In this paper, we present a novel approach for redefining distances and later re-ranking images aiming to improve the effectiveness of CBIR systems. In our approach, distance among images are redefined based on the similarity of their ranked lists. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method
    BibTeX:
    @inproceedings{Pedronette2011CAIP,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da S. Torres},
      title = {Image Re-ranking and Rank Aggregation Based on Similarity of Ranked Lists},
      booktitle = {14th International Conference on Computer Analysis of Images and Patterns (CAIP)},
      year = {2011},
      pages = {369--376},
      doi = {http://dx.doi.org/10.1007/978-3-642-23672-3\_45}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Exploiting Contextual Information for Rank Aggregation 2011 IEEE International Conference on Image Processing, pp. 97-100  inproceedings DOI  
    Abstract: This paper presents a novel rank aggregation approach based on contextual information aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) tasks. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information and then re-rank collection images. We conducted several experiments involving shape, color, and texture descriptors. We also evaluated our method in comparison to other rank aggregation approaches. Experimental results demonstrate the effectiveness of our method.
    BibTeX:
    @inproceedings{Pedronette2011ICIP,
      author = {Daniel C. G. Pedronette and Ricardo da S. Torres},
      title = {Exploiting Contextual Information for Rank Aggregation},
      booktitle = {IEEE International Conference on Image Processing},
      year = {2011},
      pages = {97--100},
      doi = {http://dx.doi.org/10.1109/ICIP.2011.6116726}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Exploiting Contextual Spaces for Image Re-Ranking and Rank Aggregation 2011 Multimedia Information Retrieval, pp. 13:1 - 13:8  inproceedings DOI  
    Abstract: The objective of Content-based Image Retrieval (CBIR) systems is to return the most similar images given an image query. In this scenario, accurately ranking collection images is of great relevance. In general, CBIR systems consider only pairwise image analysis, that is, compute similarity measures considering only pair of images, ignoring the rich information encoded in the relations among several images. This paper presents a novel re-ranking approach based on contextual spaces aiming to improve the effectiveness of CBIR tasks, by exploring relations among images. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information. The re-ranking method can also be applied to other tasks, such as: (i) for combining ranked lists obtained by using different image descriptors (rank aggregation); and (ii) for combining post-processing methods. We conducted several experiments involving shape, color, and texture descriptors and comparisons to other post-processing methods. Experimental results demonstrate the effectiveness of our method.
    BibTeX:
    @inproceedings{Pedronette2011ICMR,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da S. Torres},
      title = {Exploiting Contextual Spaces for Image Re-Ranking and Rank Aggregation},
      booktitle = {Multimedia Information Retrieval},
      year = {2011},
      pages = {13:1 -- 13:8},
      doi = {http://dx.doi.org/10.1145/1991996.1992009}
    }
    
    Penatti, O.A.B., Valle, E. & da S. Torres, R. Encoding Spatial Arrangement of Visual Words 2011 16th Iberoamerican Congress (CIARP), pp. 240-247  inproceedings DOI  
    Abstract: This paper presents a new approach to encode spatial-relationship information of visual words in the well-known visual dictionary model. The current most popular approach to describe images based on visual words is by means of bags-of-words which do not encode any spatial information. We propose a graceful way to capture spatial-relationship information of visual words that encodes the spatial arrangement of every visual word in an image. Our experiments show the importance of the spatial information of visual words for image classification and show the gain in classification accuracy when using the new method. The proposed approach creates opportunities for further improvements in image description under the visual dictionary model.
    BibTeX:
    @inproceedings{Penatti2011CIARP,
      author = {Otávio Augusto Bizetto Penatti and Eduardo Valle and Ricardo da S. Torres},
      title = {Encoding Spatial Arrangement of Visual Words},
      booktitle = {16th Iberoamerican Congress (CIARP)},
      year = {2011},
      pages = {240--247},
      doi = {http://dx.doi.org/10.1007/978-3-642-25085-9\_28}
    }
    
    dos Santos, J.A., da Silva, A.T., da S. Torres, R., Falcão, A.X., Magalhães, Lé.P. & Lamparelli, R.A.C. Interactive Classification of Remote Sensing Images by Using Optimum-Path Forest and Genetic Programming 2011 14th International Conference on Computer Analysis of Images and Patterns (CAIP), pp. 300-307  inproceedings DOI  
    Abstract: The use of remote sensing images as a source of information in agribusiness applications is very common. In those applications, it is fundamental to know how the space occupation is. However, identification and recognition of crop regions in remote sensing images are not trivial tasks yet. Although there are automatic methods proposed to that, users very often prefer to identify regions manually. That happens because these methods are usually developed to solve specific problems, or, when they are of general purpose, they do not yield satisfying results. This work presents a new interactive approach based on relevance feedback to recognize regions of remote sensing. Relevance feedback is a technique used in content-based image retrieval (CBIR) tasks. Its objective is to aggregate user preferences to the search process. The proposed solution combines the Optimum-Path Forest (OPF) classifier with composite descriptors obtained by a Genetic Programming (GP) framework. The new approach has presented good results with respect to the identification of pasture and coffee crops, overcoming the results obtained by a recently proposed method and the traditional Maximimun Likelihood algorithm.
    BibTeX:
    @inproceedings{Santos2011CAIP,
      author = {Jefersson Alex dos Santos and André Tavares da Silva and Ricardo da S. Torres and Alexandre X. Falcão and Léo Pini Magalhães and Rubens A. C. Lamparelli},
      title = {Interactive Classification of Remote Sensing Images by Using Optimum-Path Forest and Genetic Programming},
      booktitle = {14th International Conference on Computer Analysis of Images and Patterns (CAIP)},
      year = {2011},
      pages = {300--307},
      doi = {http://dx.doi.org/10.1007/978-3-642-23678-5\_35}
    }
    
    Teodoro, G., Valle, E., Mariano, N., da S. Torres, R. & Jr., W.M. Adaptive parallel approximate similarity search for responsive multimedia retrieval 2011 20th ACM Conference on Information and Knowledge Management (CIKM), pp. 495-504  inproceedings DOI  
    Abstract: This paper introduces Hypercurves, a flexible framework for providing similarity search indexing to high throughput multimedia services. Hypercurves efficiently and effectively answers k-nearest neighbor searches on multigigabyte high-dimensional databases. It supports massively parallel processing and adapts at runtime its parallelization regimens to keep answer times optimal for either low and high demands. In order to achieve its goals, Hypercurves introduces new techniques for selecting parallelism configurations and allocating threads to computation cores, including hyperthreaded cores. Its efficiency gains are throughly validated on a large database of multimedia descriptors, where it presented near linear speedups and superlinear scaleups. The adaptation reduces query response times in 43% and 74% for both platforms tested, when compared to the best static parallelism regimens.
    BibTeX:
    @inproceedings{Teodoro2011CIKM,
      author = {George Teodoro and Eduardo Valle and Nathan Mariano and Ricardo da S. Torres and Wagner Meira Jr.},
      title = {Adaptive parallel approximate similarity search for responsive multimedia retrieval},
      booktitle = {20th ACM Conference on Information and Knowledge Management (CIKM)},
      year = {2011},
      pages = {495--504},
      doi = {http://dx.doi.org/10.1145/2063576.2063651}
    }
    
    Akune, F., Valle, E. & da S. Torres, R. MONORAIL: A Disk-Friendly Index for Huge Descriptor Databases 2010 20th International Conference on Pattern Recognition, (ICPR), pp. 4145-4148  inproceedings DOI  
    Abstract: We propose MONORAIL, an indexing scheme for very large multimedia descriptor databases. Our index is based on the Hilbert curve, which is able to map the high-dimensional space of those descriptors to a single dimension. Instead of using several curves to mitigate boundary effects, we use a single curve with several surrogate points for each descriptor. Thus, we are able to reduce the random accesses to the bare minimum. In a rigorous empirical comparison with another method based on multiple surrogates, ours shows a significant improvement, due to our careful choice of the surrogate points.
    BibTeX:
    @inproceedings{Akune2010ICPR,
      author = {Fernando Akune and Eduardo Valle and Ricardo da S. Torres},
      title = {MONORAIL: A Disk-Friendly Index for Huge Descriptor Databases},
      booktitle = {20th International Conference on Pattern Recognition, (ICPR)},
      year = {2010},
      pages = {4145--4148},
      doi = {http://dx.doi.org/10.1109/ICPR.2010.1008}
    }
    
    Almeida, J., da S. Torres, R. & Leite, N.J. Rapid Video Summarization on Compressed Video 2010 IEEE International Symposium on Multimedia (ISM), pp. 113-120  inproceedings DOI  
    Abstract: Recent advances in technology have increased the availability of video data, creating a strong requirement for efficient systems to manage those materials. Making efficient use of video information requires that data be accessed in a user-friendly way. This has been the goal of a quickly evolving research area known as video summarization. Most of existing techniques to address the problem of summarizing a video sequence have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are two extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for video summarization that works in the compressed domain. The proposed method is based on both exploiting visual features extracted from the video stream and on using a simple and fast algorithm to summarize the video content. Results from a rigorous empirical comparison with a subjective evaluation show that our approach produces video summaries with superior quality relative to the state-of-the-art solutions and in a computational time that allows on-the-fly usage.
    BibTeX:
    @inproceedings{Almeida2010ISM,
      author = {Jurandy Almeida and Ricardo da S. Torres and Neucimar J. Leite},
      title = {Rapid Video Summarization on Compressed Video},
      booktitle = {IEEE International Symposium on Multimedia (ISM)},
      year = {2010},
      pages = {113--120},
      doi = {http://dx.doi.org/10.1109/ISM.2010.25}
    }
    
    Almeida, J., Minetto, R., Almeida, T., da S. Torres, R. & Leite, N.J. Estimation of Camera Parameters in Videos Sequence with a Large Amount of Scene Motion 2010 17th International Conference on Systems, Signals, and Image Processing (IWSSIP), pp. 348-351  inproceedings  
    Abstract: Most of existing techniques to estimate camera motion is based on analysis of the optical flow. However, such methods can be inaccurate and/or inefficiently when applied in video sequences which have a large amount of motion or a large number of scene changes. In this paper, we present an approach to estimate camera motion based on analysis of local invariant features. Such features are robust across a substantial range of affine distortion. Experiments on synthesized video clips with a fully controlled environment show that our technique is more effective than the optical flow-based approaches for estimating camera motion with a large amount of scene motion.
    BibTeX:
    @inproceedings{Almeida2010IWSSIP,
      author = {Jurandy Almeida and Rodrigo Minetto and Tiago Almeida and Ricardo da S. Torres and Neucimar J. Leite},
      title = {Estimation of Camera Parameters in Videos Sequence with a Large Amount of Scene Motion},
      booktitle = {17th International Conference on Systems, Signals, and Image Processing (IWSSIP)},
      year = {2010},
      pages = {348--351}
    }
    
    Faria, F.A., Papa, J.P., da S. Torres, R. & Falcão, A.X. Multimodal Pattern Recognition Through Particle Swarm Optimization 2010 17th International Conference on Systems, Signals, and Image Processing (IWSSIP), pp. 134-137  inproceedings  
    Abstract: In this paper we introduce the idea of descriptor combination by Particle Swarm Optimization and its applications for classification purposes using a recently pattern recognition technique called Optimum-Path Forest (OPF), which interprets the samples of the dataset as the nodes of a given graph, and each arc is weighted by the distance between the corresponding nodes. The method combines different color descriptors for classification in the COREL dataset to further weight the OPF graph arcs by these combined similarities. We show that our proposed approach can provides better accuracies than the individual ones obtained by each descriptor.
    BibTeX:
    @inproceedings{Faria2010IWSSIP,
      author = {Fabio A. Faria and João P. Papa and Ricardo da S. Torres and Alexandre X. Falcão},
      title = {Multimodal Pattern Recognition Through Particle Swarm Optimization},
      booktitle = {17th International Conference on Systems, Signals, and Image Processing (IWSSIP)},
      year = {2010},
      pages = {134--137}
    }
    
    Faria, F.A., Veloso, A., de Almeida, H.M., Valle, E., da S. Torres, R., Gonçalves, M.A. & Jr., W.M. Learning to rank for content-based image retrieval 2010 Multimedia Information Retrieval (MIR), pp. 285-294  inproceedings DOI  
    Abstract: In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking strategy used by many CBIR systems is to employ image content descriptors, so that returned images that are most similar to the query image are placed higher in the rank. While this strategy is well accepted and widely used, improved results may be obtained by combining multiple image descriptors. In this paper we explore this idea, and introduce algorithms that learn to combine information coming from different descriptors. The proposed learning to rank algorithms are based on three diverse learning techniques: Support Vector Machines (CBIR-SVM), Genetic Programming (CBIR-GP), and Association Rules (CBIR-AR). Eighteen image content descriptors(color, texture, and shape information) are used as input and provided as training to the learning algorithms. We performed a systematic evaluation involving two complex and heterogeneous image databases (Corel e Caltech) and two evaluation measures (Precision and MAP). The empirical results show that all learning algorithms provide significant gains when compared to the typical ranking strategy in which descriptors are used in isolation. We concluded that, in general, CBIR-AR and CBIR-GP outperforms CBIR-SVM. A fine-grained analysis revealed the lack of correlation between the results provided by CBIR-AR and the results provided by the other two algorithms, which indicates the opportunity of an advantageous hybrid approach.
    BibTeX:
    @inproceedings{Faria2010MIR,
      author = {Fabio Augusto Faria and Adriano Veloso and Humberto Mossri de Almeida and Eduardo Valle and Ricardo da S. Torres and Marcos André Gonçalves and Wagner Meira Jr.},
      title = {Learning to rank for content-based image retrieval},
      booktitle = {Multimedia Information Retrieval (MIR)},
      year = {2010},
      pages = {285--294},
      doi = {http://dx.doi.org/10.1145/1743384.1743434}
    }
    
    Garcia, V.B. & da S. Torres, R. A Shape Descriptor based on Scale-invariant Multiscale Fractal Dimension 2010
    Vol. 25th International Conference on Computer Vision Theory and Applications, pp. 185-190 
    inproceedings DOI  
    Abstract: This paper proposes a new scale-invariant shape descriptor based on the Multiscale Fractal Dimension (MFD). The MFD is a curve that describes boundary complexity and self-affinity characteristics by obtaining fractal dimension values as function of Euclidean morphologic dilation radii. Using this concept, which guarantees rotation and translation invariance, we introduce a new scale-invariant descriptor that is obtained by selecting a relevant fragment of this curve using a sliding window. The novel shape descriptor is compared with the Multiscale Fractal Dimension and four other shape descriptors. Experimental results demonstrate that the new descriptor is scale-invariant and yields very good results in terms of effectiveness performace when compared with well-known shape descriptors.
    BibTeX:
    @inproceedings{Garcia2010VISAPP,
      author = {Vítor Baccetti Garcia and Ricardo da S. Torres},
      title = {A Shape Descriptor based on Scale-invariant Multiscale Fractal Dimension},
      booktitle = {5th International Conference on Computer Vision Theory and Applications},
      publisher = {SciTePress},
      year = {2010},
      volume = {2},
      pages = {185--190},
      doi = {http://dx.doi.org/10.5220/0002831301850190}
    }
    
    Gil, F.B., Kozievitch, N.P. & da S. Torres, R. A Geographic Annotation Service for Biodiversity Systems 2010 XI Brazilian Symposium on Geoinformatics (Geoinfo), pp. 33-44  inproceedings  
    Abstract: Biodiversity studies are often based on the use of data associated with field observations. These data are usually associated with a geographic location. Most of existing biodiversity information systems provides support for storing and querying geographic data. Annotation services, in general, are not supported. This paper presents an annotation Web service to correlate biodiversity data and geographic information. We use superimposed information concepts for constructing a Web service for annotating vector geographic data. The Web service specification includes the definition of a generic API for handling annotations and the definition of a data model for storing them. The solution was validated through the implementation of a prototype for the biodiversity area considering a potential usage scenario.
    BibTeX:
    @inproceedings{Gil2010Geoinfo,
      author = {Fabiana B. Gil and Nádia P. Kozievitch and Ricardo da S. Torres},
      title = {A Geographic Annotation Service for Biodiversity Systems},
      booktitle = {XI Brazilian Symposium on Geoinformatics (Geoinfo)},
      year = {2010},
      pages = {33--44}
    }
    
    Kozievitch, N.P., da S. Torres, R., Park, S.H., Fox, E.A., Short, N., Abbott, L., Misra, S. & Hsiao, M. Rethinking Fingerprint Evidence Through Integration of Very Large Digital Libraries 2010 Third Workshop on Very Large Digital Libraries, 14th European Conference on Research and Advanced Technologies on Digital Libraries  inproceedings  
    Abstract: Fingerprints play a key role in biometrics and forensic sci- ence because of their uniqueness. Essential is contextual integration of fingerprint evidence from different sources, which involves composing, reusing, and aggregating a large amount of information. Thus, this pa- per (1) describes different types of fingerprint information from a digital library perspective; (2) investigates compound object concepts as used in connection with fingerprints; and (3) presents a preliminary integration of very large fingerprint digital libraries.
    BibTeX:
    @inproceedings{Kozievitch2010WVLDL-ECDL,
      author = {Nádia P. Kozievitch and Ricardo da S. Torres and Sung Hee Park and Edward A. Fox and Nathan Short and Lynn Abbott and Supratik Misra and Michael Hsiao},
      title = {Rethinking Fingerprint Evidence Through Integration of Very Large Digital Libraries},
      booktitle = {Third Workshop on Very Large Digital Libraries, 14th European Conference on Research and Advanced Technologies on Digital Libraries},
      year = {2010}
    }
    
    Macário, C.G.N., dos Santos, J.A., Medeiros, C.B. & da S. Torres, R. Annotating data to support decision-making: a case study 2010 6th Workshop on Geographic Information Retrieval, (GIR)  inproceedings DOI  
    Abstract: Georeferenced data are a key factor in many decision-making systems. However, their interpretation is user and context dependent so that, for each situation, data analysts have to interpret them, a time-consuming task. One approach to alleviate this task, is the use of semantic annotations to store the produced information. Annotating data is however hard to perform and prone to errors, especially when executed manually. This difficulty increases with the amount of data to annotate. Moreover, annotation requires multi-disciplinary collaboration of researchers, with access to heterogeneous and distributed data sources and scientific computations. This paper illustrates our solution to approach this problem by means of a case study in agriculture. It shows how our implementation of a framework to automate the annotation of geospatial data can be used to process real data from remote sensing images and other official Brazilian data sources.
    BibTeX:
    @inproceedings{Macario2010GIR,
      author = {Carla Geovana N. Macário and Jefersson Alex dos Santos and Claudia Bauzer Medeiros and Ricardo da S. Torres},
      title = {Annotating data to support decision-making: a case study},
      booktitle = {6th Workshop on Geographic Information Retrieval, (GIR)},
      year = {2010},
      doi = {http://dx.doi.org/10.1145/1722080.1722106}
    }
    
    Murthy, U., Kozievitch, N.P., Fox, E.A., da S. Torres, R. & Hallerm, E.M. SUPERIDR: A Tablet PC Tool for Image Description and Retrieval 2010 Workshop on the Impact of Pen-Based Technology on Education (WIPTE)  inproceedings  
    Abstract: SuperIDR is a tablet-PC-based superimposed image description and retrieval tool, which combines text and visual content-based image description and retrieval. It allows users to select and mark parts of images and associate them with text annotations. Annotations can be entered using either pen-based or keypad-based input. Later, users can retrieve information through text- and content-based search on textual descriptions, annotations, images, and parts of images. We developed SuperIDR as an aid to species identification for the fisheries domain, working with freshwater fishes of Virginia. We adapted the tool to work with parasite images and descriptions. We tested both versions in fisheries and biology classes at Virginia Tech and UNICAMP (Brazil) and found the tool to be a useful aid to species identification. In this paper, we provide an overview of SuperIDR and the evaluations that we conducted.
    BibTeX:
    @inproceedings{Murthy2010WIPTE,
      author = {Uma Murthy and Nádia P. Kozievitch and Edward. A. Fox and Ricardo da S. Torres and Eric M. Hallerm},
      title = {SUPERIDR: A Tablet PC Tool for Image Description and Retrieval},
      booktitle = {Workshop on the Impact of Pen-Based Technology on Education (WIPTE)},
      year = {2010}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Exploiting Contextual Information for Image Re-ranking 2010 15th Iberoamerican Congress on Pattern Recognition, (CIARP), pp. 541-548  inproceedings DOI  
    Abstract: This paper presents a novel re-ranking approach based on contextual information used to improve the effectiveness of Content-Based Image Retrieval (CBIR) tasks. In our approach, image processing techniques are applied to ranked lists defined by CBIR descriptors. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method.
    BibTeX:
    @inproceedings{Pedronette2010CIARP,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da S. Torres},
      title = {Exploiting Contextual Information for Image Re-ranking},
      booktitle = {15th Iberoamerican Congress on Pattern Recognition, (CIARP)},
      year = {2010},
      pages = {541--548},
      doi = {http://dx.doi.org/10.1007/978-3-642-16687-7\_71}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Distances Correlation for Re-Ranking in Content-Based Image Retrieval 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images, pp. 1-8  inproceedings DOI  
    Abstract: Content-based image retrieval relies on the use of efficient and effective image descriptors. One of the most important components of an image descriptor is concerned with the distance function used to measure how similar two images are. This paper presents a clustering approach based on distances correlation for computing the similarity among images. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method.
    BibTeX:
    @inproceedings{Pedronette2010SIBGRAPI,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da S. Torres},
      title = {Distances Correlation for Re-Ranking in Content-Based Image Retrieval},
      booktitle = {23rd SIBGRAPI Conference on Graphics, Patterns and Images},
      year = {2010},
      pages = {1--8},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2010.9}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Shape Retrieval using Contour Features and Distance Optimization 2010
    Vol. 25th International Conference on Computer Vision Theory and Applications, pp. 197-202 
    inproceedings DOI  
    Abstract: This paper presents a shape descriptor based on a set of features computed for each point of an object contour. We also present an algorithm for distance optimization based on the similarity among ranked lists. Experiments were conducted on two well-known data sets: MPEG-7 and Kimia. Experimental results demonstrate that the combination of the two methods is very effective and yields better results than recently proposed shape descriptors.
    BibTeX:
    @inproceedings{Pedronette2010VISAPP,
      author = {Daniel Carlos Guimarães Pedronette and Ricardo da S. Torres},
      title = {Shape Retrieval using Contour Features and Distance Optimization},
      booktitle = {5th International Conference on Computer Vision Theory and Applications},
      publisher = {SciTePress},
      year = {2010},
      volume = {2},
      pages = {197--202},
      doi = {http://dx.doi.org/10.5220/0002837201970202}
    }
    
    dos Santos, J.A., Faria, F.A., Calumby, R.T., da Silva Torres, R. & Lamparelli, R.A.C. A Genetic Programming approach for coffee crop recognition 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3418-3421  inproceedings DOI  
    Abstract: This work presents a new approach for automatic recognition of coffee crops in RSIs. The method applies an approach based on Genetic Programming (GP) to combine texture and spectral information encoded by image descriptors. Experiments show that the proposed method yields slightly better results than the traditional MaxVer approach.
    BibTeX:
    @inproceedings{Santos2010IGARSS,
      author = {Jefersson Alex dos Santos and Fabio Augusto Faria and Rodrigo Tripodi Calumby and Ricardo da Silva Torres and Rubens A. C. Lamparelli},
      title = {A Genetic Programming approach for coffee crop recognition},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      year = {2010},
      pages = {3418--3421},
      doi = {http://dx.doi.org/10.1109/IGARSS.2010.5650273}
    }
    
    dos Santos, J.A., Penatti, O.A.B. & da S. Torres, R. Evaluating the Potential of Texture and Color Descriptors for Remote Sensing Image Retrieval and Classification 2010
    Vol. 25th International Conference on Computer Vision Theory and Applications, pp. 203-208 
    inproceedings DOI  
    Abstract: Classifying Remote Sensing Images (RSI) is a hard task. There are automatic approaches whose results normally need to be revised. The identification and polygon extraction tasks usually rely on applying classification strategies that exploit visual aspects related to spectral and texture patterns identified in RSI regions. There are a lot of image descriptors proposed in the literature for content-based image retrieval purposes that may be useful for RSI classification. This paper presents a comparative study to evaluate the potential of using successful color and texture image descriptors for remote sensing retrieval and classification. Seven descriptors that encode texture information and twelve color descriptors that can be used to encode spectral information were selected. We perform experiments to evaluate the effectiveness of these descriptors, considering image retrieval and classification tasks. To evaluate descriptors in classification tasks, we also propose a methodology based on KNN classifier. Experiments demonstrate that Joint Auto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition (SID) and Quantized Compound Change Histogram (QCCH) yield the best results.
    BibTeX:
    @inproceedings{Santos2010VISAPP,
      author = {Jefersson Alex dos Santos and Otávio Augusto Bizetto Penatti and Ricardo da S. Torres},
      title = {Evaluating the Potential of Texture and Color Descriptors for Remote Sensing Image Retrieval and Classification},
      booktitle = {5th International Conference on Computer Vision Theory and Applications},
      publisher = {SciTePress},
      year = {2010},
      volume = {2},
      pages = {203--208},
      doi = {http://dx.doi.org/10.5220/0002843402030208}
    }
    
    Almeida, J., Minetto, R., Almeida, T.A., da S. Torres, R. & Leite, N.J. Robust Estimation of Camera Motion using Optical Flow Models 2009 International Symposium on Advances in Visual Computing, pp. 435-446  inproceedings DOI  
    Abstract: The estimation of camera motion is one of the most important aspects for video processing, analysis, indexing, and retrieval. Most of existing techniques to estimate camera motion are based on optical flow methods in the uncompressed domain. However, to decode and to analyze a video sequence is extremely time-consuming. Since video data are usually available in MPEG-compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for estimating camera motion in MPEG video sequences. Our technique relies on linear combinations of optical flow models. The proposed method first creates prototypes of optical flow, and then performs a linear decomposition on the MPEG motion vectors, which is used to estimate the camera parameters. Experiments on synthesized and real-world video clips show that our technique is more effective than the state-of-the-art approaches for estimating camera motion in MPEG video sequences.
    BibTeX:
    @inproceedings{Almeida2009ISVC,
      author = {Jurandy Almeida and Rodrigo Minetto and Tiago A. Almeida and Ricardo da S. Torres and Neucimar J. Leite},
      title = {Robust Estimation of Camera Motion using Optical Flow Models},
      booktitle = {International Symposium on Advances in Visual Computing},
      year = {2009},
      pages = {435--446},
      doi = {http://dx.doi.org/10.1007/978-3-642-10331-5\_41}
    }
    
    Montoya-Zegarra, J.A., Papa, J.P., Leite, N.J., da S. Torres, R. & Falcão, A.X. Novel Approaches for Exclusive and Continuous Fingerprint Classification 2009 3rd Pacific-Rim Symposium on Image and Video Technology, pp. 386-397  inproceedings DOI  
    Abstract: This paper proposes novel exclusive and continuous approaches to guide the search and the retrieval in fingerprint image databases. Both approaches are useful to perform a coarse level classification of fingerprint images before fingerprint authentication tasks. Our approaches are characterized by: (1) texture image descriptors based on pairs of multi-resolution decomposition methods that encode effectively global and local fingerprint information, with similarity measures used for fingerprint matching purposes, and (2) a novel multi-class object recognition method based on the Optimum Path Forest classifier. Experiments were carried out on the standard NIST-4 dataset aiming to study the discriminative and scalability capabilities of our approaches. The high classification rates allow us demonstrate the feasibility and validity of our approaches for characterizing fingerprint images accurately.
    BibTeX:
    @inproceedings{Montoya-Zegarra2009PSIVT,
      author = {Javier A. Montoya-Zegarra and João P. Papa and Neucimar J. Leite and Ricardo da S. Torres and Alexandre X. Falcão},
      title = {Novel Approaches for Exclusive and Continuous Fingerprint Classification},
      booktitle = {3rd Pacific-Rim Symposium on Image and Video Technology},
      year = {2009},
      pages = {386--397},
      doi = {http://dx.doi.org/10.1007/978-3-540-92957-4\_34}
    }
    
    Murthy, U., Fox, E.A., Chen, Y., Hallerman, E., da Silva Torres, R., Ramos, E.J. & Falcão, T.R.C. Superimposed Image Description and Retrieval for Fish Species Identification 2009 13th European Conference on Digital Libraries, pp. 285-296  inproceedings DOI  
    Abstract: Fish species identification is critical to the study of fish ecology and management of fisheries. Traditionally, dichotomous keys are used for fish identification. The keys consist of questions about the observed specimen. Answers to these questions lead to more questions till the reader identifies the specimen. However, such keys are incapable of adapting or changing to meet different fish identification approaches, and often do not focus upon distinguishing characteristics favored by many field ecologists and more user-friendly field guides. This makes learning to identify fish difficult for Ichthyology students. Students usually supplement the use of the key with other methods such as making personal notes, drawings, annotated fish images, and more recently, fish information websites, such as Fishbase. Although these approaches provide useful additional content, it is dispersed across heterogeneous sources and can be tedious to access. Also, most of the existing electronic tools have limited support to manage user created content, especially that related to parts of images such as markings on drawings and images and associated notes. We present SuperIDR, a superimposed image description and retrieval tool, developed to address some of these issues. It allows users to associate parts of images with text annotations. Later, they can retrieve images, parts of images, annotations, and image descriptions through text- and content-based image retrieval. We evaluated SuperIDR in an undergraduate Ichthyology class as an aid to fish species identification and found that the use of SuperIDR yielded a higher likelihood of success in species identification than using traditional methods, including the dichotomous key, fish web sites, notes, etc.
    BibTeX:
    @inproceedings{Murthy2009ECDL,
      author = {Uma Murthy and Edward A. Fox and Yinlin Chen and Eric Hallerman and Ricardo da Silva Torres and Evandro J. Ramos and Tiago R. C. Falcão},
      title = {Superimposed Image Description and Retrieval for Fish Species Identification},
      booktitle = {13th European Conference on Digital Libraries},
      year = {2009},
      pages = {285--296},
      doi = {http://dx.doi.org/10.1007/978-3-642-04346-8\_28}
    }
    
    Santos, K.C.L., de Almeida, H.M., Gonçalves, M.A. & da Silva Torres, R. Recuperacão de Imagens da Web Utilizando Múltiplas Evidências Textuais e Programação Genética 2009 XXIV Brazilian Symposium on Databases, pp. 91-105  inproceedings URL 
    Abstract: This paper describes an image retrieval framework that employs a evolutionary approach. The proposed framework explores Genetic Programming for combining multiple textual sources of evidence associated to Web images. Experiments performed with a collection extracted from the Web formed by 1,223,829 pages and 254,495 images showed that the evolutionary framework was able to overcome the Belief Networks (Bayesian Model) with gains upper to 100% for precision (pn and MAP) and recall measures.
    BibTeX:
    @inproceedings{Santos2009SBBD,
      author = {Katia C. L. Santos and Humberto Mossri de Almeida and Marcos André Gonçalves and Ricardo da Silva Torres},
      title = {Recuperacão de Imagens da Web Utilizando Múltiplas Evidências Textuais e Programação Genética},
      booktitle = {XXIV Brazilian Symposium on Databases},
      year = {2009},
      pages = {91--105},
      url = {http://www.lbd.dcc.ufmg.br:8080/colecoes/sbbd/2009/007.pdf}
    }
    
    dos Santos, J.A., Lamparelli, R. & da S. Torres, R. Using Relevance Feedback for Classifying Remote Sensing Images 2009 Brazilian Remote Sensing Symposium, pp. 7909-7916  inproceedings  
    Abstract: This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions which are of interest. Furthermore, a genetic programming approach is used to learn user preferences and combine image region descriptors that encode spectral and texture properties. The approach to classify images can be divided into four main steps: (i) image partition and region feature extraction, (ii) identification of the partitions which are of interest, (iii) image segmentation, and (iv) region vectorization. This work describes the obtained results from the first two main steps: partition/extraction of image features and recognition of partitions of interest. So, in the first step the image are partitioned into tiles. Each tile is considered as an independent image and this process starts by the indication of a query image by the user. This query image is assumed to present the same texture and spectral properties of the RSI regions which are of interest. A similarity search is performed and the most similar tiles are returned to the user. The user then indicates if the returned tiles are relevant or non-relevant. By using this feedback, the classification system learns the user needs and tunes itself in order to improve the results in the next iteration. This process is repeated until the user is satisfied with the result. Experiments demonstrate that the proposed method is effective and suitable for image classification tasks.
    BibTeX:
    @inproceedings{Santos2009SBSR,
      author = {Jefersson A. dos Santos and Rubens Lamparelli and Ricardo da S. Torres},
      title = {Using Relevance Feedback for Classifying Remote Sensing Images},
      booktitle = {Brazilian Remote Sensing Symposium},
      year = {2009},
      pages = {7909--7916}
    }
    
    Almeida, J.G., Rocha, A., da S. Torres, R. & Goldestein, S. Making Colors Worth more than a Thousand Words 2008 The 23th Annual ACM Symposium on Applied Computing, pp. 1184-1190  inproceedings DOI  
    Abstract: Content-based image retrieval (CBIR) is a challenging task. Common techniques use only low-level features. However, these solutions can lead to the so-called `semantic gap' problem: images with high feature similarities may be different in terms of user perception. In this paper, our objective is to retrieve images based on color cues which may present some affine transformations. For that, we present CSIR: a new method for comparing images based on discrete distributions of distinctive color and scale image regions. We validate the technique using images with a large range of viewpoints, partial occlusion, changes in illumination, and various domains.
    BibTeX:
    @inproceedings{Almeida2008ACMSAC,
      author = {Jurandy G. Almeida and Anderson Rocha and Ricardo da S. Torres and Siome Goldestein},
      title = {Making Colors Worth more than a Thousand Words},
      booktitle = {The 23th Annual ACM Symposium on Applied Computing},
      year = {2008},
      pages = {1184--1190},
      doi = {http://dx.doi.org/10.1145/1363686.1363961}
    }
    
    Escalona-Cuaresma, M.J., Torres-Zenteno, A., Gutierrez, J., Martins, E., da S. Torres, R. & Baranauskas, M.C.C. A Development Process for Web Geographic Information System: A Case of Study 2008
    Vol. HCIProceedings of the 10th International Conference on Enterprise Information Systems (ICEIS 2008), pp. 112-117 
    inproceedings DOI  
    Abstract: This paper introduces a process for developing Web GIS (Geographic Information Systems) applications. This process integrates the NDT (Navigational Development Techniques) approach with some of the Organizational Semiotic models. The use of the proposed development process is illustrated for a real application: the construction of the WebMaps system. WebMaps is a Web GIS system whose main goal is to support harvest planning in Brazil.
    BibTeX:
    @inproceedings{Escalona-Cuaresma2008ICEIS,
      author = {Maria J. Escalona-Cuaresma and Arturo Torres-Zenteno and Javier Gutierrez and Eliane Martins and Ricardo da S. Torres and Maria Cecília C. Baranauskas},
      title = {A Development Process for Web Geographic Information System: A Case of Study},
      booktitle = {Proceedings of the 10th International Conference on Enterprise Information Systems (ICEIS 2008)},
      publisher = {SciTePress},
      year = {2008},
      volume = {HCI},
      pages = {112--117},
      note = {ISBN: 978-989-8111-40-1},
      doi = {http://dx.doi.org/10.5220/0001668101120117}
    }
    
    Ferreira, C.D., da S. Torres, R., Goncalves, M.A. & Fan, W. Image Retrieval with Relevance Feedback based on Genetic Programming 2008 XXIII Brazilian Symposium on Databases, pp. 120-134  inproceedings  
    Abstract: This paper presents a new content-based image retrieval framework with relevance feedback. This framework employs Genetic Programming to discover a combination of descriptors that better characterizes the user perception of image similarity. Several experiments were conducted to validate the proposed framework. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed framework was compared with three other relevance feedback methods regarding their efficiency and effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.
    BibTeX:
    @inproceedings{Ferreira2008SBBD,
      author = {Cristiano D. Ferreira and Ricardo da S. Torres and Marcos A. Goncalves and Weiguo Fan},
      title = {Image Retrieval with Relevance Feedback based on Genetic Programming},
      booktitle = {XXIII Brazilian Symposium on Databases},
      year = {2008},
      pages = {120--134}
    }
    
    Montoya-Zegarra, J.A., Beek-Pepper, J.C., Leite, N.J., da S. Torres, R. & Falcão, A.X. Combining Global with Local Texture Information for Image Retrieval Applications 2008 IEEE International Symposium on Multimedia, pp. 148-153  inproceedings DOI  
    Abstract: This paper proposes a new texture descriptor to guide the search and retrieval in image databases. It extracts rich information from global and local primitives of textured images. At a higher level, the global macro-features in textured images are characterized by exploiting the multiresolution properties of the Steerable Pyramid Decomposition. By doing this, the global texture configurations are highlighted. At a finer level, the local arrangements of texture micro-patterns are encoded by the Local Binary Pattern operator.Experiments were carried out on the standard Vistex dataset aiming to compare our descriptors against popular texture extraction methods with regard to their retrieval accuracies. The comparative evaluations allowed us to show the superior descriptive properties of our feature representation methods.
    BibTeX:
    @inproceedings{Montoya-Zegarra2008ISM,
      author = {Javier A. Montoya-Zegarra and Jan C. Beek-Pepper and Neucimar J. Leite and Ricardo da S. Torres and Alexandre X. Falcão},
      title = {Combining Global with Local Texture Information for Image Retrieval Applications},
      booktitle = {IEEE International Symposium on Multimedia},
      year = {2008},
      pages = {148--153},
      doi = {http://dx.doi.org/10.1109/ISM.2008.113}
    }
    
    Moreno, M.A.M. & da S. Torres, R. Recuperacão de Imagem Utilizando Descritores Baseados em Esqueleto 2008 IV Workshop de Visão Computacional  inproceedings  
    Abstract: Este artigo apresenta duas técnicas de caracterizacão de formas baseada em esqueletos. Experimentos ocnduzidos mostram um aumento da eficácia quando as técnicas propostas são combinadas com descritores de forma tradicionais.
    BibTeX:
    @inproceedings{Moreno2008WVC,
      author = {Marcio A. M. Moreno and Ricardo da S. Torres},
      title = {Recuperacão de Imagem Utilizando Descritores Baseados em Esqueleto},
      booktitle = {IV Workshop de Visão Computacional},
      year = {2008}
    }
    
    Pedronette, D.C.G. & da S. Torres, R. Uma Plataforma de Servicos de Recomendacão para Bibliotecas Digitais 2008 XIII Brazilian Symposium on Databases, pp. 253-267  inproceedings  
    Abstract: This paper presents a platform for recommendation services, called RecS-DL, to support the use of recommendation tools by digital libray applications. The proposed RecS-DL platform is independent of application domain, technology, and recommendation techniques. The recommendation services offered by the platform can be easily incorporated into digital libraries systems. Furthermore, new recommendation engines can also be plugged into the platform in a dynamic way. We present the results obtained from experiments conducted with real digital libraries and from evaluations made by potential users. Experimental results show that the platform facilitates the interoperability of recommendation tools in digital libraries systems.
    BibTeX:
    @inproceedings{Pedronette2008SBBD,
      author = {Daniel C. G. Pedronette and Ricardo da S. Torres},
      title = {Uma Plataforma de Servicos de Recomendacão para Bibliotecas Digitais},
      booktitle = {XIII Brazilian Symposium on Databases},
      year = {2008},
      pages = {253--267}
    }
    
    Penatti, O.B. & da S. Torres, R. Color Descriptors for Web Image Retrieval: a Comparative Study 2008 XXI Brazilian Symposium on Computer Graphics and Image Processing, pp. 163-170  inproceedings DOI  
    Abstract: This paper presents a comparative study of color descriptors for content-based image retrieval on the Web. Several image descriptors were compared theoretically and the most relevant ones were implemented and tested in two different databases. The main goal was to find out the best descriptors for Web image retrieval. Descriptors are compared according to the extraction and distance functions complexities, the compactness of feature vectors, and the ability to retrieve relevant images.
    BibTeX:
    @inproceedings{Penatti2008SIBGRAPI,
      author = {Otávio B. Penatti and Ricardo da S. Torres},
      title = {Color Descriptors for Web Image Retrieval: a Comparative Study},
      booktitle = {XXI Brazilian Symposium on Computer Graphics and Image Processing},
      year = {2008},
      pages = {163--170},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2008.20}
    }
    
    Rocha, A., Almeida, J.G., Nascimento, M., da S. Torres, R. & Goldenstein, S. Efficient and Flexible Cluster-and-Search for CBIR 2008 Advanced Concepts for Intelligent Vision Systems, pp. 77-88  inproceedings DOI  
    Abstract: Content-Based Image Retrieval is a challenging problem both in terms of effectiveness and efficiency. In this paper, we present a flexible cluster-and-search approach that is able to reuse any previously proposed image descriptor as long as a suitable similarity function is provided. In the clustering step, the image data set is clustered using a hybrid divisive-agglomerative hierarchical clustering technique. The obtained clusters are organized in a tree that can be traversed efficiently using the similarity function associated with the chosen image descriptors. Our experiments have shown that we can improve search-time performance by a factor of 10 or more, at the cost of small loss in effectiveness (typically less than 15%) when compared to the state-of-the-art solutions.
    BibTeX:
    @inproceedings{Rocha2008ACIVS,
      author = {Anderson Rocha and Jurandy G. Almeida and Mario Nascimento and Ricardo da S. Torres and Siome Goldenstein},
      title = {Efficient and Flexible Cluster-and-Search for CBIR},
      booktitle = {Advanced Concepts for Intelligent Vision Systems},
      year = {2008},
      pages = {77--88},
      doi = {http://dx.doi.org/10.1007/978-3-540-88458-3\_8}
    }
    
    dos Santos, J.A., Ferreira, C.D. & da S. Torres, R. A Genetic Programming Approach for Relevance Feedback in Region-based Image Retrieval Systems 2008 XXI Brazilian Symposium on Computer Graphics and Image Processing, pp. 155-162  inproceedings DOI  
    Abstract: This paper presents a new relevance feedback method for content-based image retrieval using local image features. This method adopts a genetic programming approach to learn user preferences and combine the region similarity values in a query session. Experiments demonstrate that the proposed method yields more effective results than the local aggregation pattern (LAP)-based relevance feedback technique.
    BibTeX:
    @inproceedings{Santos2008SIBGRAPI,
      author = {Jefersson A. dos Santos and Cristiano D. Ferreira and Ricardo da S. Torres},
      title = {A Genetic Programming Approach for Relevance Feedback in Region-based Image Retrieval Systems},
      booktitle = {XXI Brazilian Symposium on Computer Graphics and Image Processing},
      year = {2008},
      pages = {155--162},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2008.15}
    }
    
    da S. Torres, R., Zegarra, J.A.M., Santos, J.A., Ferreira, C.D., Penatti, O.A.B., Andaló, F.A. & Almeida, J.G. Recuperacão de Imagens: Desafios e Novos Rumos 2008 XXXV Seminário Integrado de Software e Hardware (SEMISH), pp. 223-237  inproceedings  
    Abstract: Huge image collections have been created, managed and stored into image databases. Given the large size of these collections it is essential to provide efficient and effective mechanisms to retrieve images. This is the objective of the so-called content-based image retrieval -- CBIR -- systems. Traditionally, these systems are based on objective criteria to represent and compare images. However, users of CBIR systems tend to use subjective elements to compare images. The use of these elements have improved the effectiveness of content- based image retrieval systems. This paper discusses approaches that incorpo- rate semantic information into content-based image retrieval process, highlighting some new challenges on this area.
    BibTeX:
    @inproceedings{Torres2008SEMISH,
      author = {Ricardo da S. Torres and Javier A. M. Zegarra and Jefersson A. Santos and Cristiano D. Ferreira and Otávio A. B. Penatti and Fernanda A. Andaló and Jurandy G. Almeida},
      title = {Recuperacão de Imagens: Desafios e Novos Rumos},
      booktitle = {XXXV Seminário Integrado de Software e Hardware (SEMISH)},
      year = {2008},
      pages = {223--237}
    }
    
    Andaló, F.A., Miranda, P.A.V., da S. Torres, R. & Falcão, A.X. Detecting Contour Saliences Using Tensor Scale 2007 IEEE International Conference on Image Processing, pp. VI349-VI352  inproceedings DOI  
    Abstract: Tensor scale is a morphometric parameter that unifies the representation of local structure thickness, orientation, and anisotropy, which can be used in several image processing tasks. This paper introduces a new application for tensor scale, which is the detection of saliences on a given contour, based on the tensor scale orientations computed for the entire object and mapped to its contour. For validation purposes, we present a shape descriptor that uses the detected contour saliences. Experimental results are provided, comparing the proposed method with our previous contour salience descriptor (CS). We show that the proposed method can be not only faster and more robust in the detection of salience points than the CS method, but also more effective as a shape descriptor.
    BibTeX:
    @inproceedings{Andalo2007ICIP,
      author = {Fernanda A. Andaló and Paulo A. V. Miranda and Ricardo da S. Torres and Alexandre X. Falcão},
      title = {Detecting Contour Saliences Using Tensor Scale},
      booktitle = {IEEE International Conference on Image Processing},
      year = {2007},
      pages = {VI349--VI352},
      doi = {http://dx.doi.org/10.1109/ICIP.2007.4379593}
    }
    
    Andaló, F.A., Miranda, P.A.V., da S. Torres, R. & Falcão, A.X. A New Shape Descriptor based on Tensor Scale 2007 8th International Symposium on Mathematical Morphology, pp. 141-152  inproceedings  
    Abstract: Tensor scale is a morphometric parameter that unifies the representation of local structure thickness, orientation, and anisotropy, which can be used in several computer vision and image processing tasks. In this paper, we exploit this concept for binary images and propose a shape descriptor that encodes region and contour properties in a very efficient way. Experimental results are provided, showing the effectiveness of the proposed descriptor, when compared to other relevant shape descriptors, with regard to their use in content-based image retrieval systems.
    BibTeX:
    @inproceedings{Andalo2007ISMM,
      author = {Fernanda A. Andaló and Paulo A. V. Miranda and Ricardo da S. Torres and Alexandre X. Falcão},
      title = {A New Shape Descriptor based on Tensor Scale},
      booktitle = {8th International Symposium on Mathematical Morphology},
      year = {2007},
      pages = {141--152}
    }
    
    Mariote, L., Medeiros, C.B. & da S. Torres, R. Diagnosing Similarity of Oscillation Trends in Time Series 2007 International Workshop on Spatial and Spatio-Temporal Data Mining, 7th IEEE International Conference on Data Mining (ICDM 2007), pp. 643-648  inproceedings DOI  
    Abstract: Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. They present many ways for describing and comparing time series, but they focus on their values. This paper concentrates on a new aspect that of describing oscillation patterns. It presents a technique for time series similarity search, based on multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of time series.
    BibTeX:
    @inproceedings{Mariote2007SSTDM,
      author = {Leonardo Mariote and Claudia B. Medeiros and R. da S. Torres},
      title = {Diagnosing Similarity of Oscillation Trends in Time Series},
      booktitle = {International Workshop on Spatial and Spatio-Temporal Data Mining, 7th IEEE International Conference on Data Mining (ICDM 2007)},
      year = {2007},
      pages = {643--648},
      doi = {http://dx.doi.org/10.1109/ICDMW.2007.28}
    }
    
    Montoya-Zegarra, J.A., Papa, J.P., Leite, N.J., da S. Torres, R. & Falcão, A.X. Rotation-invariant Texture Recognition 2007 International Symposium on Visual Computing, pp. 193-204  inproceedings DOI  
    Abstract: This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method.
    BibTeX:
    @inproceedings{Montoya-Zegarra2007ISVC,
      author = {Javier A. Montoya-Zegarra and João P. Papa and Neucimar J. Leite and Ricardo da S. Torres and A. X. Falcão},
      title = {Rotation-invariant Texture Recognition},
      booktitle = {International Symposium on Visual Computing},
      year = {2007},
      pages = {193--204},
      doi = {http://dx.doi.org/10.1007/978-3-540-76856-2\_19}
    }
    
    Montoya-Zegarra, J.A., Leite, N.J. & da S. Torres, R. Rotation-Invariant and Scale-Invariant Steerable Pyramid Decomposition for Texture Image Retrieval 2007 XX Brazilian Symposium on Computer Graphics and Image Processing, pp. 121-128  inproceedings DOI  
    Abstract: This paper proposes a new rotation-invariant and scale-invariant representation for texture image retrieval based on steerable pyramid decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering either the dominant orientation or dominant scale of the input textures. Experiments were conducted on the Brodatz database aiming to compare our approach to the conventional steerable pyramid decomposition, and a proposal for texture characterization based on Gabor wavelets with regard to their retrieval effectiveness. Results demonstrate the superiority of the proposed method in rotated and scaled image datasets.
    BibTeX:
    @inproceedings{Montoya-Zegarra2007SIBGRAPI,
      author = {Javier A. Montoya-Zegarra and Neucimar J. Leite and Ricardo da S. Torres},
      title = {Rotation-Invariant and Scale-Invariant Steerable Pyramid Decomposition for Texture Image Retrieval},
      booktitle = {XX Brazilian Symposium on Computer Graphics and Image Processing},
      year = {2007},
      pages = {121--128},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2007.42}
    }
    
    Murthy, U., Gorton, D., da S. Torres, R., calves, M.A.G., Fox, E.A. & Delcambre, L. Extending the 5S Digital Library (DL) Framework: From a Minimal DL towards a DL Reference Model 2007 1st Workshop on Digital Library Foundations, ACM IEEE Joint Conference on Digital Libraries  inproceedings  
    Abstract: In this paper, we describe ongoing research in three DL projects that build upon a common foundation - the 5S DL framework. In each project, we extend the 5S framework to provide specifications for a particular type of DL service and/or system - finally, moving towards a DL reference model. In the first project, we are working on formalizing content-based image retrieval services in a DL. In the second project, we are developing specifications for a superimposed information-supported DL (combining annotation, hypertext, and knowledge management technologies). In the third effort, we have used the 5S framework to generate a practical DL system based on the DSpace software.
    BibTeX:
    @inproceedings{Murthy2007WDLF-JCDL,
      author = {Uma Murthy and Douglas Gorton and Ricardo da S. Torres and Marcos A. Goncalves and Edward A. Fox and Lois Delcambre},
      title = {Extending the 5S Digital Library (DL) Framework: From a Minimal DL towards a DL Reference Model},
      booktitle = {1st Workshop on Digital Library Foundations, ACM IEEE Joint Conference on Digital Libraries},
      year = {2007}
    }
    
    Penatti, O.B. & da S. Torres, R. Descritor de Relacionamento Espacial baseado em Particões 2007 XXVI Concurso de Trabalhos de Iniciacão Científica, XXVII Congresso da Sociedade Brasileira de Computacão   inproceedings  
    Abstract: Spatial relationships can be fundamental for image recognition and retrieval, being useful for geographic and medical applications, for instance. This paper presents a new spatial relationship descriptor for content-based image retrieval. The new descriptor presented is based on partitioning the space of analysis into quadrants and on counting the number of object points in each partition. Experiments demonstrated that the proposed descriptor is more effective than the other descriptors studied in this work.
    BibTeX:
    @inproceedings{Penatti2007CTIC-SBC,
      author = {Otávio B. Penatti and Ricardo da S. Torres},
      title = {Descritor de Relacionamento Espacial baseado em Particões},
      booktitle = {XXVI Concurso de Trabalhos de Iniciacão Científica, XXVII Congresso da Sociedade Brasileira de Computacão },
      year = {2007}
    }
    
    Shen, R., Vemuri, N.S., Fan, W., da S. Torres, R. & Fox, E.A. Exploring Digital Libraries: Integrating Browsing, Searching, and Visualization 2006 Proceedings of the 6th ACM/IEEE Joint Conference on Digital libraries, pp. 1-10  inproceedings DOI  
    Abstract: Exploring services for digital libraries (DLs) include two major paradigms, browsing and searching, as well as other services such as clustering and visualization. In this paper, we formalize and generalize DL exploring services within a DL theory. We develop theorems to indicate that browsing and searching can be converted or mapped to each other under certain conditions. The theorems guide the design and implementation of exploring services for an integrated archaeological DL, ETANA-DL. Its integrated browsing and searching can support users in moving seamlessly between these operations, minimizing context switching, and keeping users focused. It also integrates browsing and searching into a single visual interface for DL exploration. A user study to evaluate ETANA-DL's exploring services helped validate our hypotheses.
    BibTeX:
    @inproceedings{Shen2006JCDL,
      author = {Rao Shen and Naga S. Vemuri and Weiguo Fan and Ricardo da S. Torres and Edward A. Fox},
      title = {Exploring Digital Libraries: Integrating Browsing, Searching, and Visualization},
      booktitle = {Proceedings of the 6th ACM/IEEE Joint Conference on Digital libraries},
      year = {2006},
      pages = {1--10},
      doi = {http://dx.doi.org/10.1145/1141753.1141755}
    }
    
    Torres-Zenteno, A.H., Martins, E., da S. Torres, R. & Escalona-Cuaresma, M.J. Teste de Desempenho em Aplicações SIG Web 2006 IX Conferencia Iberoamericana de Software Engineering (CIbSE 2006), pp. 449-462  inproceedings  
    Abstract: Este artigo propõe um modelo de processo de teste de desempenho para aplicacões SIG Web. O modelo considera os casos de uso mais críticos ou de maior risco quanto ao desempenho de um sistema para a criacão de cenários de testes. Além disso, prevê a utilizacão de ferramentas livres para automatizacão de etapas do processo de avaliacão. O modelo foi aplicado ao projeto WebMaps, que é uma aplicacão SIG Web cuja finalidade é auxiliar seus usuários no planejamento agrícola a partir de regiões de interesse. Os resultados preliminares obtidos indicam que os testes foram úteis na identificacão de problemas da arquitetura preliminar do sistema.
    BibTeX:
    @inproceedings{Torres-Zenteno2006IDEAS,
      author = {Arturo Henry Torres-Zenteno and Eliane Martins and Ricardo da S. Torres and María José Escalona-Cuaresma},
      title = {Teste de Desempenho em Aplicações SIG Web},
      booktitle = {IX Conferencia Iberoamericana de Software Engineering (CIbSE 2006)},
      year = {2006},
      pages = {449-462}
    }
    
    Carvalho, A.C.P.L.F., Brayner, A., Loureiro, A., Furtado, A.L., v. Staa, A., Lucena, C.J.P., S., C.S., Medeiros, C.M.B., Lucchesi, C.L., Silva, E.S., Wagner, F.R., Simon, I., Wainer, J., Maldonado, J.C., Oliveira, J.P.M., Ribeiro, L., Velho, L., calves, M.A.G., Baranauskas, M.C.C., Mattoso, M., Ziviani, N., Navaux, P.O.A., da S. Torres, R., Almeida, V.A.F., Jr., W.M. & Kohayakawa, Y. Grandes Desafios da Pesquisa em Computacão no Brasil -- 2006 - 2016 2006 Seminário Grandes Desafios da Sociedade Brasileira de Computacão  inproceedings  
    BibTeX:
    @inproceedings{Torres2006GrandesDesafios,
      author = {A. C. P. L. F. Carvalho and A. Brayner and A. Loureiro and A. L. Furtado and A. v. Staa and C. J. P. Lucena and C. S. S. and C. M. B. Medeiros and C. L. Lucchesi and E. S. Silva and F. R. Wagner and I. Simon and J. Wainer and J. C. Maldonado and J. P. M. Oliveira and L. Ribeiro and L. Velho and M. A. Goncalves and M. C. C. Baranauskas and M. Mattoso and N. Ziviani and P. O. A. Navaux and R. da S. Torres and V. A. F. Almeida and W. Meira Jr. and Y. Kohayakawa},
      title = {Grandes Desafios da Pesquisa em Computacão no Brasil -- 2006 - 2016},
      booktitle = {Seminário Grandes Desafios da Sociedade Brasileira de Computacão},
      year = {2006}
    }
    
    da. da S. Torres, R. Sistemas de Informacão para o Gerenciamento de Imagens: Aplicacões e Desafios de Pesquisa 2006 Grandes Desafios da Sociedade Brasileira de Computacão  inproceedings  
    BibTeX:
    @inproceedings{Torres2006sbc,
      author = {Ricardo da. da S. Torres},
      title = {Sistemas de Informacão para o Gerenciamento de Imagens: Aplicacões e Desafios de Pesquisa},
      booktitle = {Grandes Desafios da Sociedade Brasileira de Computacão},
      year = {2006}
    }
    
    Freitas, R.B. & da S. Torres, R. OntoSAIA: Um ambiente Baseado em Ontologias para Recuperacão e Anotacão Semi-Automática de Imagens 2005 First Workshop on Digital Libraries (Primeiro Workshop de Bibliotecas Digitais), XX Brazilian Symposium on Databases, pp. 60-69  inproceedings  
    Abstract: This article proposes the use of image content, keywords and ontologies to improve the image annotation and retrieval processes through the enhancement of the user's knowledge of an image database. It proposes an architecture of a flexible system capable of dealing with multiple ontologies and multiple image content descriptors to help these tasks. The validation of the idea is being done through the implementation, in Java, of the software OntoSAIA.
    BibTeX:
    @inproceedings{Freitas2005WDL-SBBD,
      author = {Ricardo B. Freitas and Ricardo da S. Torres},
      title = {OntoSAIA: Um ambiente Baseado em Ontologias para Recuperacão e Anotacão Semi-Automática de Imagens},
      booktitle = {First Workshop on Digital Libraries (Primeiro Workshop de Bibliotecas Digitais), XX Brazilian Symposium on Databases},
      year = {2005},
      pages = {60--69}
    }
    
    Miranda, P.A.V., da S. Torres, R. & Falcão, A.X. TSD: A Shape Descriptor Based on a Distribution of Tensor Scale Local Orientation 2005 18th Brazilian Symposium on Computer Graphics and Image, pp. 139-146  inproceedings DOI  
    Abstract: We present tensor scale descriptor (TSD) -- a shape descriptor for content-based image retrieval, registration, and analysis. TSD exploits the notion of local structure thickness, orientation, and anisotropy as represented by the largest ellipse centered at each image pixel and within the same homogeneous region. The proposed method uses the normalized histogram of the local orientation (the angle of the ellipse) at regions of high anisotropy and thickness within a certain interval. It is shown that TSD is invariant to rotation and to some reasonable level of scale changes. Experimental results with a fish database are presented to illustrate and validate the method.
    BibTeX:
    @inproceedings{Miranda2005SIBGRAPI,
      author = {Paulo A. V. Miranda and Ricardo da S. Torres and Alexandre X. Falcão},
      title = {TSD: A Shape Descriptor Based on a Distribution of Tensor Scale Local Orientation},
      booktitle = {18th Brazilian Symposium on Computer Graphics and Image},
      year = {2005},
      pages = {139--146},
      doi = {http://dx.doi.org/10.1109/SIBGRAPI.2005.51}
    }
    
    da S. Torres, R., Silva, C.G., Medeiros, C.B. & da Rocha, H.V. Visual structures for image browsing 2003 Proceedings of the 12th International Conference on Information and Knowledge Management, pp. 49-55  inproceedings DOI  
    Abstract: Content-Based Image Retrieval (CBIR) presents several challenges and has been subject to extensive research from many domains, such as image processing or database systems. Database researchers are concerned with indexing and querying, whereas image processing experts worry about extracting appropriate image descriptors. Comparatively little work has been done on designing user interfaces for CBIR systems. This, in turn, has a profound effect on these systems since the concept of image similarity is strongly influenced by user perception. This paper describes an initial effort to fill this gap, combining recent research in CBIR and Information Visualization, studied from a Human-Computer Interface perspective. It presents two visualization techniques based on Spiral and Concentric Rings implemented in a CBIR system to explore query results. The approach is centered on keeping user focus on both the query image, and the most similar retrieved images. Experiments conducted so far suggest that the proposed visualization strategies improves system usability.
    BibTeX:
    @inproceedings{Torres2003CIKM,
      author = {Ricardo da S. Torres and Celmar G. Silva and Claudia Bauzer Medeiros and Heloisa Vieira da Rocha},
      title = {Visual structures for image browsing},
      booktitle = {Proceedings of the 12th International Conference on Information and Knowledge Management},
      year = {2003},
      pages = {49--55},
      doi = {http://dx.doi.org/10.1145/956863.956874}
    }
    
    da S. Torres, R., Picado, E.M., Falcão, A.X. & da Fontoura Costa, L. Effective Image Retrieval by Shape Saliences 2003 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003), pp. 167-174  inproceedings DOI  
    Abstract: Content-based image retrieval (CBIR) systems have been developed aiming at enabling users to search and retrieve images based on their properties such as shape, color and texture. We are concerned with shape-based image retrieval. Here, we discuss a recently proposed shape descriptor, called contour saliences, defined as the influence areas of its higher curvature points. We introduce a robust approach to estimate contour saliences by exploiting the relation between a contour and its skeleton, modifies the original definition to include the location and the value of saliences along the contour, and proposes a new metric to compare contour saliences. We also evaluate the effectiveness of the proposed descriptor with respect to Fourier descriptors, curvature scale space and moment invariants.
    BibTeX:
    @inproceedings{Torres2003SIBGRAPI,
      author = {Ricardo da S. Torres and Eduardo M. Picado and Alexandre X. Falcão and Luciano da Fontoura Costa},
      title = {Effective Image Retrieval by Shape Saliences},
      booktitle = {16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003)},
      year = {2003},
      pages = {167--174},
      doi = {http://dx.doi.org/10.1109/SIBGRA.2003.1241005}
    }
    
    da S. Torres, R., Falcão, A.X. & da F. Costa, L. Shape Description by Image Foresting Transform 2002
    Vol. 2Proceedings of the 14th International Conference on Digital Signal Processing, pp. 1089-1092 
    inproceedings DOI  
    Abstract: The image foresting transform (IFT) is a unified and effective graph-based approach to the design of image-processing operations, often with considerable efficiency gains over published algorithms. This paper extends the applications of the Euclidean IFT to two recently proposed shape descriptors: saliences and multiscale fractal dimension. It explains how to obtain the salience information and the multiscale fractal dimension of contours and skeletons and presents their comparison in terms of robustness and separability.
    BibTeX:
    @inproceedings{Torres2002DSP,
      author = {Ricardo da S. Torres and Alexandre X. Falcão and Luciano da F. Costa},
      title = {Shape Description by Image Foresting Transform},
      booktitle = {Proceedings of the 14th International Conference on Digital Signal Processing},
      year = {2002},
      volume = {2},
      pages = {1089--1092},
      doi = {http://dx.doi.org/10.1109/ICDSP.2002.1028280}
    }
    

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