Unsupervised Manifold Learning for Video Genre Retrieval

Detalhes bibliográficos
Autor(a) principal: Almeida, Jurandy
Data de Publicação: 2014
Outros Autores: Pedronette, Daniel C. G. [UNESP], Penatti, Otavio A. B., BayroCorrochano, E., Hancock, E.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/184746
Resumo: This paper investigates the perspective of exploiting pairwise similarities to improve the performance of visual features for video genre retrieval. We employ manifold learning based on the reciprocal neighborhood and on the authority of ranked lists to improve the retrieval of videos considering their genre. A comparative analysis of different visual features is conducted and discussed. We experimentally show in the dataset of 14,838 videos from the MediaEval benchmark that we can achieve considerable improvements in results. In addition, we also evaluate how the late fusion of different visual features using the same manifold learning scheme can improve the retrieval results.
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spelling Unsupervised Manifold Learning for Video Genre Retrievalvideo genre retrievalranking methodsmanifold learningThis paper investigates the perspective of exploiting pairwise similarities to improve the performance of visual features for video genre retrieval. We employ manifold learning based on the reciprocal neighborhood and on the authority of ranked lists to improve the retrieval of videos considering their genre. A comparative analysis of different visual features is conducted and discussed. We experimentally show in the dataset of 14,838 videos from the MediaEval benchmark that we can achieve considerable improvements in results. In addition, we also evaluate how the late fusion of different visual features using the same manifold learning scheme can improve the retrieval results.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fed Univ Sao Paulo UNIFESP, Inst Sci & Technol, BR-12231280 Sao Jose Dos Campos, SP, BrazilSao Paulo State Univ, Dept Stat, Appl Math & Computat, BR-13506900 Rio Claro, SP, BrazilAdv Technol SAMSUNG Res Inst, BR-13097160 Campinas, SP, BrazilSao Paulo State Univ, Dept Stat, Appl Math & Computat, BR-13506900 Rio Claro, SP, BrazilFAPESP: 2013/08645-0SpringerUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Adv Technol SAMSUNG Res InstAlmeida, JurandyPedronette, Daniel C. G. [UNESP]Penatti, Otavio A. B.BayroCorrochano, E.Hancock, E.2019-10-04T12:29:42Z2019-10-04T12:29:42Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject604-612Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 604-612, 2014.0302-9743http://hdl.handle.net/11449/184746WOS:000346407400074Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014info:eu-repo/semantics/openAccess2021-10-22T21:10:06Zoai:repositorio.unesp.br:11449/184746Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-22T21:10:06Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Unsupervised Manifold Learning for Video Genre Retrieval
title Unsupervised Manifold Learning for Video Genre Retrieval
spellingShingle Unsupervised Manifold Learning for Video Genre Retrieval
Almeida, Jurandy
video genre retrieval
ranking methods
manifold learning
title_short Unsupervised Manifold Learning for Video Genre Retrieval
title_full Unsupervised Manifold Learning for Video Genre Retrieval
title_fullStr Unsupervised Manifold Learning for Video Genre Retrieval
title_full_unstemmed Unsupervised Manifold Learning for Video Genre Retrieval
title_sort Unsupervised Manifold Learning for Video Genre Retrieval
author Almeida, Jurandy
author_facet Almeida, Jurandy
Pedronette, Daniel C. G. [UNESP]
Penatti, Otavio A. B.
BayroCorrochano, E.
Hancock, E.
author_role author
author2 Pedronette, Daniel C. G. [UNESP]
Penatti, Otavio A. B.
BayroCorrochano, E.
Hancock, E.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
Adv Technol SAMSUNG Res Inst
dc.contributor.author.fl_str_mv Almeida, Jurandy
Pedronette, Daniel C. G. [UNESP]
Penatti, Otavio A. B.
BayroCorrochano, E.
Hancock, E.
dc.subject.por.fl_str_mv video genre retrieval
ranking methods
manifold learning
topic video genre retrieval
ranking methods
manifold learning
description This paper investigates the perspective of exploiting pairwise similarities to improve the performance of visual features for video genre retrieval. We employ manifold learning based on the reciprocal neighborhood and on the authority of ranked lists to improve the retrieval of videos considering their genre. A comparative analysis of different visual features is conducted and discussed. We experimentally show in the dataset of 14,838 videos from the MediaEval benchmark that we can achieve considerable improvements in results. In addition, we also evaluate how the late fusion of different visual features using the same manifold learning scheme can improve the retrieval results.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2019-10-04T12:29:42Z
2019-10-04T12:29:42Z
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 Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 604-612, 2014.
0302-9743
http://hdl.handle.net/11449/184746
WOS:000346407400074
identifier_str_mv Progress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 604-612, 2014.
0302-9743
WOS:000346407400074
url http://hdl.handle.net/11449/184746
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
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 604-612
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
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