A multiple labeling-based optimum-path forest for video content classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorFederal University of Mato Grosso Do sul-
Autor(es): dc.creatorPereira, Luis A.M.-
Autor(es): dc.creatorPapa, J. Paulo-
Autor(es): dc.creatorAlmeida, Jurandy-
Autor(es): dc.creatorTorres, Ricardo Da S.-
Autor(es): dc.creatorAmorim, Willian Paraguassu-
Data de aceite: dc.date.accessioned2025-08-21T18:33:58Z-
Data de disponibilização: dc.date.available2025-08-21T18:33:58Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2013-12-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI.2013.53-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/227415-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/227415-
Descrição: dc.descriptionMultiple-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. © 2013 IEEE.-
Descrição: dc.descriptionDepartment of Computing Sao Paulo State University, Bauru-
Descrição: dc.descriptionInstitute of Computing University of Campinas, Campinas-
Descrição: dc.descriptionInstitute of Computing Federal University of Mato Grosso Do sul, Campo Grande-
Descrição: dc.descriptionDepartment of Computing Sao Paulo State University, Bauru-
Formato: dc.format334-340-
Idioma: dc.languageen-
Relação: dc.relationBrazilian Symposium of Computer Graphic and Image Processing-
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Palavras-chave: dc.subjectImage motion analysis-
Palavras-chave: dc.subjectMulti-label learning-
Palavras-chave: dc.subjectOptimum-Path Forest-
Palavras-chave: dc.subjectVideo signal classification-
Título: dc.titleA multiple labeling-based optimum-path forest for video content classification-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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