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@InProceedings{PereiraPapAlmTorAmo:2013:MuLaOp,
               author = "Pereira, Luis Augusto Martins and Papa, Joao Paulo and Almeida, 
                         Jurandy and Torres, Ricardo da Silva and Amorim, Willian 
                         Paraguassu",
          affiliation = "{UNESP - Univ Estadual Paulista} and {UNESP - Univ Estadual 
                         Paulista} and {University of Campinas} and {University of 
                         Campinas} and {Federal University of Mato Grosso do Sul}",
                title = "A Multiple Labeling-based Optimum-Path Forest for Video Content 
                         Classification",
            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 = "Image motion analysis, Video signal classification, multi-label 
                         learning, Optimum-Path Forest.",
             abstract = "Multiple-labeling classification approaches attempt to handle 
                         applications that associate more than one label to a given sample. 
                         Since we have an increasing number of systems that are guided by 
                         such assumption, in this paper we have presented a 
                         multiple-labeling approach for the Optimum-Path Forest (OPF) 
                         classifier based on the problem transformation method. In order to 
                         validate our proposal, a multi-labeled video classification 
                         dataset has been used to compare OPF against three other 
                         classifiers and another variant of the OPF classifier based on a 
                         k-neighborhood. The results have shown the validity of the 
                         OPF-based classifiers for multi-labeling classification 
                         problems.",
  conference-location = "Arequipa, Peru",
      conference-year = "Aug. 5-8, 2013",
             language = "en",
           targetfile = "camera_ready.pdf",
        urlaccessdate = "2020, Nov. 26"
}


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