Unsupervised manifold learning for video genre retrieval
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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:29462024-08-05T18:39:04.085608Repositório 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. 1611-3349 0302-9743 2-s2.0-84949157291 |
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 |
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.source.none.fl_str_mv |
Scopus 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_ |
1808128959889539072 |