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@InProceedings{PedronetteTorr:2010:DiCoRe,
               author = "Pedronette, Daniel Carlos Guimar{\~a}es and Torres, Ricardo da 
                         S.",
          affiliation = "{RECOD Lab - Institute of Computing - University of Campinas} and 
                         {RECOD Lab - Institute of Computing - University of Campinas}",
                title = "Distances Correlation for Re-Ranking in Content-Based Image 
                         Retrieval",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "content-based image retrieval, re-ranking, distance optimization, 
                         clustering.",
             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.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "SIBGRAPFinal_PDFExpress.pdf",
        urlaccessdate = "2020, Nov. 29"
}


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