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Reference TypeConference Proceedings
Last Update2013:
Metadata Last Update2020: administrator
Citation KeyPereiraPapAlmTorAmo:2013:MuLaOp
TitleA Multiple Labeling-based Optimum-Path Forest for Video Content Classification
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
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 :: -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
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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