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		<citationkey>PereiraPapAlmTorAmo:2013:MuLaOp</citationkey>
		<author>Pereira, Luis Augusto Martins,</author>
		<author>Papa, Joao Paulo,</author>
		<author>Almeida, Jurandy,</author>
		<author>Torres, Ricardo da Silva,</author>
		<author>Amorim, Willian Paraguassu,</author>
		<affiliation>UNESP - Univ Estadual Paulista</affiliation>
		<affiliation>UNESP - Univ Estadual Paulista</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>Federal University of Mato Grosso do Sul</affiliation>
		<title>A Multiple Labeling-based Optimum-Path Forest for Video Content Classification</title>
		<conferencename>Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)</conferencename>
		<year>2013</year>
		<editor>Boyer, Kim,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Nedel, Luciana,</editor>
		<editor>Silva, Claudio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Aug. 5-8, 2013</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Arequipa, Peru</conferencelocation>
		<keywords>Image motion analysis, Video signal classification, multi-label learning, Optimum-Path Forest.</keywords>
		<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.</abstract>
		<language>en</language>
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		<e-mailaddress>papa@fc.unesp.br</e-mailaddress>
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