Static Video Summarization through Optimum-Path Forest Clustering

Detalhes bibliográficos
Autor(a) principal: Martins, G. B. [UNESP]
Data de Publicação: 2014
Outros Autores: Afonso, L. C. S. [UNESP], Osaku, D., Almeida, Jurandy, Papa, João Paulo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-319-12568-8_108
http://hdl.handle.net/11449/116222
Resumo: This paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.
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spelling Static Video Summarization through Optimum-Path Forest Clusteringvideo summarizationoptimum-path forestclusteringThis paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.Sao Paulo State Univ, UNESP, Dept Comp, BR-17033360 Bauru, SP, BrazilSao Paulo State Univ, UNESP, Dept Comp, BR-17033360 Bauru, SP, BrazilSpringerUniversidade Estadual Paulista (Unesp)Martins, G. B. [UNESP]Afonso, L. C. S. [UNESP]Osaku, D.Almeida, JurandyPapa, João Paulo [UNESP]2015-03-18T15:52:36Z2015-03-18T15:52:36Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject893-900http://dx.doi.org/10.1007/978-3-319-12568-8_108Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 893-900, 2014.0302-9743http://hdl.handle.net/11449/11622210.1007/978-3-319-12568-8_108WOS:0003464074001089039182932747194Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 20140,295info:eu-repo/semantics/openAccess2024-04-23T16:11:26Zoai:repositorio.unesp.br:11449/116222Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:26Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Static Video Summarization through Optimum-Path Forest Clustering
title Static Video Summarization through Optimum-Path Forest Clustering
spellingShingle Static Video Summarization through Optimum-Path Forest Clustering
Martins, G. B. [UNESP]
video summarization
optimum-path forest
clustering
title_short Static Video Summarization through Optimum-Path Forest Clustering
title_full Static Video Summarization through Optimum-Path Forest Clustering
title_fullStr Static Video Summarization through Optimum-Path Forest Clustering
title_full_unstemmed Static Video Summarization through Optimum-Path Forest Clustering
title_sort Static Video Summarization through Optimum-Path Forest Clustering
author Martins, G. B. [UNESP]
author_facet Martins, G. B. [UNESP]
Afonso, L. C. S. [UNESP]
Osaku, D.
Almeida, Jurandy
Papa, João Paulo [UNESP]
author_role author
author2 Afonso, L. C. S. [UNESP]
Osaku, D.
Almeida, Jurandy
Papa, João Paulo [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Martins, G. B. [UNESP]
Afonso, L. C. S. [UNESP]
Osaku, D.
Almeida, Jurandy
Papa, João Paulo [UNESP]
dc.subject.por.fl_str_mv video summarization
optimum-path forest
clustering
topic video summarization
optimum-path forest
clustering
description This paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2015-03-18T15:52:36Z
2015-03-18T15:52:36Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-319-12568-8_108
Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 893-900, 2014.
0302-9743
http://hdl.handle.net/11449/116222
10.1007/978-3-319-12568-8_108
WOS:000346407400108
9039182932747194
url http://dx.doi.org/10.1007/978-3-319-12568-8_108
http://hdl.handle.net/11449/116222
identifier_str_mv Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 893-900, 2014.
0302-9743
10.1007/978-3-319-12568-8_108
WOS:000346407400108
9039182932747194
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 893-900
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1799965235916832768

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