The Unsupervised Distance Learning Framework (UDLF) is a software which enables an easy use and evaluation of unsupervised learning methods, specially for multimedia retrieval tasks. The framework defines a broad model, allowing the implementation of different unsupervised methods and supporting diverse file formats for input and output. Executions and experiments can be easily defined by setting a configuration file. The framework also includes the evaluation of the retrieval results exporting visual output results, computing effectiveness and efficiency measures. The source-code is public available, such that anyone can freely access, use, change, and share the software under the terms of the GPLv2 license.
VALEM, L. P.; PEDRONETTE, D. C. G. . An Unsupervised Distance Learning Framework for Multimedia Retrieval. In: International Conference on Multimedia Retrieval (ICMR) (accepted), 2017.
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Currently, seven different unsupervised learning methods are implemented:PEDRONETTE, D. C. G.; ALMEIDA, J.; TORRES, R. da S.. A graph-based ranked-list model for unsupervised distance learning on shape retrieval. Pattern Recognition Letters, v. 83, p. 357-367, 2016.
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In this paper, we introduce a novel graph-based approach for iterative distance learning in shape retrieval tasks. The proposed method is based on the combination of graphs defined in terms of multiple ranked lists. Effectiveness analysis performed in three widely used shape datasets demonstrate that the proposed graph-based ranked-list model yields significant gains (up to +55.52%) when compared with the use of shape descriptors in isolation.
PEDRONETTE, D. C. G.; TORRES, R. S. . A correlation graph approach for unsupervised manifold learning in image retrieval tasks. Neurocomputing, v. 208, p. 66-79, 2016.
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PEDRONETTE, D. C. G.; TORRES, R. S. . Unsupervised Manifold Learning By Correlation Graph and Strongly Connected Components for Image Retrieval. In: IEEE International Conference on Image Processing (ICIP), 2014.
Selected among the 9 finalists for the Best Paper Award (from 1219 accepted).
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Effectively measuring the similarity among images is a challenging problem in image retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents an unsupervised manifold learning algorithm that takes into account the intrinsic dataset geometry for defining a more effective distance among images. The dataset structure is modeled in terms of a Correlation Graph (CG) and analyzed using Strongly Connected Components (SCCs). While the Correlation Graph adjacency provides a precise but strict similarity relationship, the Strongly Connected Components analysis expands these relationships considering the dataset geometry.
VALEM, L. P. ; PEDRONETTE, D. C. G. . Unsupervised Similarity Learning through Cartesian Product of Ranking References for Image Retrieval Tasks. In: Conference on Graphics, Images and Patterns (SIBGRAPI) 2016.
Best Paper Award.
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Similarity learning approaches capable of improving the effectiveness of retrieval in an unsupervised way are indispensable. A novel method, called Cartesian Product of Ranking References (CPRR), is proposed with this objective in this paper. The proposed method uses Cartesian product operations based on rank information for exploiting the underlying structure of datasets. Only subsets of ranked lists are required, demanding low computational efforts.
VALEM, L. P. ; PEDRONETTE, D. C. G. ; TORRES, R. da S. ; BORIN, E. ; ALMEIDA, J. . Effective, Efficient, and Scalable Unsupervised Distance Learning in Image Retrieval Tasks. In: ACM International Conference on Multimedia Retrieval (ICMR), 2015.
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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 computing.
PEDRONETTE, D. C. G.; PENATTI, O. A. B. ; TORRES, R. S. . Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks. Image and Vision Computing, v. 32, p. 120-130, 2014.
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In this paper, we propose a Reciprocal kNN Graph algorithm that considers the relationships among ranked lists in the context of a k-reciprocal neighborhood. The similarity is propagated among neighbors considering the geometry of the dataset manifold. The proposed method can be used both for re-ranking and rank aggregation tasks.
PEDRONETTE, D. C. G.; TORRES, R. S. . Image re-ranking and rank aggregation based on similarity of ranked lists. Pattern Recognition, v. 46, p. 2350-2360, 2013.
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OKADA, C. Y. ; PEDRONETTE, D. C. G. ; TORRES, R. S. . Unsupervised Distance Learning by Rank Correlation Measures for Image Retrieval. In: ACM International Conference on Multimedia Retrieval (ICMR), 2015.
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In Content-based Image Retrieval (CBIR) systems, ranking accurately collection images is of great relevance. Users are interested in the returned images placed at the first positions, which usually are the most relevant ones. Collection images are ranked in redincreasing order of their distance to the query pattern (e.g., query image) defined by users. Therefore, the effectiveness of these systems is very dependent on the accuracy of the distance function adopted. In this paper, we present a novel context-based approach for redefining distances and later re-ranking images aiming to improve the effectiveness of CBIR systems. In our approach, distances among images are redefined based on the similarity of their ranked lists.
PEDRONETTE, D. C. G.; TORRES, R. da S. . Exploiting contextual information for image re-ranking and rank aggregation. International Journal of Multimedia Information Retrieval, v. 1, p. 115-128, 2012.
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Content-based Image Retrieval (CBIR) systems aims to retrieve the most similar images in a col- lection, given a query image. Since users are interested in the returned images placed at the first positions of ranked lists (which usually are the most relevant ones), the effectiveness of these systems is very dependent on the accuracy of ranking approaches. This paper presents a novel re-ranking algorithm aiming to exploit contextual information for improving the effectiveness of rankings computed by CBIR systems. In our approach, ranked lists and distance scores are used to create context images, later used for retrieving contextual information.
