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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2013/07.05.08.50
%2 sid.inpe.br/sibgrapi/2013/07.05.08.50.47
%A Pereira, Luis Augusto Martins,
%A Papa, Joao Paulo,
%A Almeida, Jurandy,
%A Torres, Ricardo da Silva,
%A Amorim, Willian Paraguassu,
%@affiliation UNESP - Univ Estadual Paulista
%@affiliation UNESP - Univ Estadual Paulista
%@affiliation University of Campinas
%@affiliation University of Campinas
%@affiliation Federal University of Mato Grosso do Sul
%T A Multiple Labeling-based Optimum-Path Forest for Video Content Classification
%B Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)
%D 2013
%E Boyer, Kim,
%E Hirata, Nina,
%E Nedel, Luciana,
%E Silva, Claudio,
%S Proceedings
%8 Aug. 5-8, 2013
%J Los Alamitos
%I IEEE Computer Society
%C Arequipa, Peru
%K Image motion analysis, Video signal classification, multi-label learning, Optimum-Path Forest.
%X 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.
%@language en
%3 camera_ready.pdf


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