@techreport{TR-IC-07-05,
  number = {IC-07-05},
  author = {Cristiano D.~Ferreira and Ricardo da S.~Torres},
  title = {Image Retrieval with Relevance Feedback based on Genetic Programming},
  month = {February},
  year = {2007}, 
  institution = {Institute of Computing, University of Campinas},
  note = {In English, 22 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       In the last years, large digital image collections are
       generated, manipulated, and stored in databases. In this
       scenery, it is very important to develop mechanisms to provide
       automatic means to retrieve images in an efficient and
       effective way. However, the subjectivity of the user perception
       of an image usually hampers a fully automatic search and
       retrieval. Relevance Feedback is one of the commonest
       approaches to overcome this difficult.
       \par 
       In this paper, a new content-based image retrieval framework
       with relevance feedback is proposed. This framework uses
       Genetic Programming (GP) to learn the user needs. The objective
       of this learning method is to find a function that combines
       different values of similarity, from distinct descriptors, and
       best encodes the user perception of image similarity. Several
       experiments are performed to validate the proposed method,
       aiming to compare our work with other relevance feedback
       techniques. The experiment results show that the proposed
       method outperforms all of them.
  }
}