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@InProceedings{LucenaFerrOlivMach:2015:AtLoAu,
               author = "Lucena, David Jones Ferreira de and Ferreira Junior, Jos{\'e} 
                         Raniery and Oliveira, Marcelo Costa and Machado, Aydano Pamponet",
          affiliation = "{Federal University of Alagoas} and {Federal University of 
                         Alagoas} and {Federal University of Alagoas} and {Federal 
                         University of Alagoas}",
                title = "Atualiza{\c{c}}{\~a}o local autom{\'a}tica de pesos de 
                         atributos para recupera{\c{c}}{\~a}o de n{\'o}dulos pulmonares 
                         similares",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Rios, Ricardo Araujo and Paiva, Afonso",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Content-based image retrieval, information retrieval, decision 
                         support, update weighing attributes, lung cancer.",
             abstract = "Lung cancer is the third most common among the types of cancer 
                         existing in the world, staying back of prostate cancer in men and 
                         breast cancer in women. Computer-Aided (CAD) systems have been 
                         built in order to help experts identify and classify lung nodules. 
                         One type of CAD that has shown good results is the Content-Based 
                         Image Retrieval (CBIR). But one of the biggest challenges of CBIR 
                         is to define the appropriate measure for evaluating the 
                         similarity, other is to find a way to address the gap between the 
                         features used by experts to evaluate the images and attributes 
                         extracted from it segmentation. This work proposes a CBIR 
                         architecture to automatically calculate the weights of the 
                         attributes based on local learning to reflect the user 
                         interpretation in image retrieval process, reducing the semantic 
                         gap and improving performance in the recovery based on content.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "pt",
                  ibi = "8JMKD3MGPBW34M/3JRLK92",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JRLK92",
           targetfile = "SIBGRAPI-VERSAO-APROVADA2.pdf",
        urlaccessdate = "2021, Dec. 04"
}


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