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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3EDJGRP
Repositorysid.inpe.br/sibgrapi/2013/07.05.08.50
Last Update2013:07.05.08.50.47 papa@fc.unesp.br
Metadatasid.inpe.br/sibgrapi/2013/07.05.08.50.47
Metadata Last Update2020:02.19.03.09.22 administrator
Citation KeyPereiraPapAlmTorAmo:2013:MuLaOp
TitleA Multiple Labeling-based Optimum-Path Forest for Video Content Classification
FormatOn-line.
Year2013
DateAug. 5-8, 2013
Access Date2020, Dec. 04
Number of Files1
Size307 KiB
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Author1 Pereira, Luis Augusto Martins
2 Papa, Joao Paulo
3 Almeida, Jurandy
4 Torres, Ricardo da Silva
5 Amorim, Willian Paraguassu
Affiliation1 UNESP - Univ Estadual Paulista
2 UNESP - Univ Estadual Paulista
3 University of Campinas
4 University of Campinas
5 Federal University of Mato Grosso do Sul
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addresspapa@fc.unesp.br
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2013-07-05 08:50:47 :: papa@fc.unesp.br -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
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Is the master or a copy?is the master
Document Stagecompleted
Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsImage motion analysis, Video signal classification, multi-label learning, Optimum-Path Forest.
AbstractMultiple-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.
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