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, Otávio A.B.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/168183
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 retrievalManifold learningRanking methodsVideo genre retrievalThis 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Institute of Science and Technology Federal University of São Paulo – UNIFESPDept. of Statistics,Applied Mathematics and Computation São Paulo State University – UNESPAdvanced Technologies SAMSUNG Research InstituteDept. of Statistics,Applied Mathematics and Computation São Paulo State University – UNESPUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)SAMSUNG Research InstituteAlmeida, JurandyPedronette, Daniel C.G. [UNESP]Penatti, Otávio A.B.2018-12-11T16:40:07Z2018-12-11T16:40:07Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject604-612Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 604-612.1611-33490302-9743http://hdl.handle.net/11449/1681832-s2.0-84949157291Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:25Zoai:repositorio.unesp.br:11449/168183Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:25Repositório 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
Manifold learning
Ranking methods
Video genre retrieval
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, Otávio A.B.
author_role author
author2 Pedronette, Daniel C.G. [UNESP]
Penatti, Otávio A.B.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
SAMSUNG Research Institute
dc.contributor.author.fl_str_mv Almeida, Jurandy
Pedronette, Daniel C.G. [UNESP]
Penatti, Otávio A.B.
dc.subject.por.fl_str_mv Manifold learning
Ranking methods
Video genre retrieval
topic Manifold learning
Ranking methods
Video genre retrieval
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
2018-12-11T16:40:07Z
2018-12-11T16:40:07Z
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 604-612.
1611-3349
0302-9743
http://hdl.handle.net/11449/168183
2-s2.0-84949157291
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8827, p. 604-612.
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url http://hdl.handle.net/11449/168183
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
0,295
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 604-612
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reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
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reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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