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@InProceedings{PedronetteTorr:2013:UnMeEs,
               author = "Pedronette, Daniel Carlos Guimar{\~a}es and Torres, Ricardo da 
                         S.",
          affiliation = "{State University of S{\~a}o Paulo (UNESP)} and {University of 
                         Campinas (UNICAMP)}",
                title = "Unsupervised measures for estimating the effectiveness of image 
                         retrieval systems",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
                         Claudio",
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "effectiveness estimation, content-based image retrieval, rank 
                         aggregation.",
             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.",
  conference-location = "Arequipa, Peru",
      conference-year = "Aug. 5-8, 2013",
             language = "en",
           targetfile = "PID2848405.pdf",
        urlaccessdate = "2020, Nov. 26"
}


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