UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL

Detalhes bibliográficos
Autor(a) principal: Guimaraes Pedronette, Daniel Carlos [UNESP]
Data de Publicação: 2014
Outros Autores: Torres, Ricardo da S., IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/184782
Resumo: This paper presents a novel manifold learning approach that takes into account the intrinsic dataset geometry. The dataset structure is modeled in terms of a Correlation Graph and analyzed using Strongly Connected Components (SCCs). The proposed manifold learning approach defines a more effective distance among images, used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach yields better results in terms of effectiveness than various methods recently proposed in the literature.
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spelling UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVALcontent-based image retrievalunsupervised anifold learningcorrelation graphThis paper presents a novel manifold learning approach that takes into account the intrinsic dataset geometry. The dataset structure is modeled in terms of a Correlation Graph and analyzed using Strongly Connected Components (SCCs). The proposed manifold learning approach defines a more effective distance among images, used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach yields better results in terms of effectiveness than various methods recently proposed in the literature.State Univ Sao Paulo UNESP, Dept Stat Appl Math & Comp, Rio Claro, BrazilUniv Campinas UNICAMP, Inst Comp, Recod Lab, Campinas, SP, BrazilState Univ Sao Paulo UNESP, Dept Stat Appl Math & Comp, Rio Claro, BrazilIeeeUniversidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Guimaraes Pedronette, Daniel Carlos [UNESP]Torres, Ricardo da S.IEEE2019-10-04T12:30:05Z2019-10-04T12:30:05Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1892-18962014 Ieee International Conference On Image Processing (icip). New York: Ieee, p. 1892-1896, 2014.1522-4880http://hdl.handle.net/11449/184782WOS:000370063602013Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 Ieee International Conference On Image Processing (icip)info:eu-repo/semantics/openAccess2021-10-22T21:54:27Zoai:repositorio.unesp.br:11449/184782Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-22T21:54:27Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
title UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
spellingShingle UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
Guimaraes Pedronette, Daniel Carlos [UNESP]
content-based image retrieval
unsupervised anifold learning
correlation graph
title_short UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
title_full UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
title_fullStr UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
title_full_unstemmed UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
title_sort UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
author Guimaraes Pedronette, Daniel Carlos [UNESP]
author_facet Guimaraes Pedronette, Daniel Carlos [UNESP]
Torres, Ricardo da S.
IEEE
author_role author
author2 Torres, Ricardo da S.
IEEE
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Estadual de Campinas (UNICAMP)
dc.contributor.author.fl_str_mv Guimaraes Pedronette, Daniel Carlos [UNESP]
Torres, Ricardo da S.
IEEE
dc.subject.por.fl_str_mv content-based image retrieval
unsupervised anifold learning
correlation graph
topic content-based image retrieval
unsupervised anifold learning
correlation graph
description This paper presents a novel manifold learning approach that takes into account the intrinsic dataset geometry. The dataset structure is modeled in terms of a Correlation Graph and analyzed using Strongly Connected Components (SCCs). The proposed manifold learning approach defines a more effective distance among images, used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach yields better results in terms of effectiveness than various methods recently proposed in the literature.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2019-10-04T12:30:05Z
2019-10-04T12:30:05Z
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 2014 Ieee International Conference On Image Processing (icip). New York: Ieee, p. 1892-1896, 2014.
1522-4880
http://hdl.handle.net/11449/184782
WOS:000370063602013
identifier_str_mv 2014 Ieee International Conference On Image Processing (icip). New York: Ieee, p. 1892-1896, 2014.
1522-4880
WOS:000370063602013
url http://hdl.handle.net/11449/184782
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2014 Ieee International Conference On Image Processing (icip)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1892-1896
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
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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