Unsupervised manifold learning by correlation graph and strongly connected components for image retrieval

Detalhes bibliográficos
Autor(a) principal: Pedronette, Daniel Carlos Guimaraes [UNESP]
Data de Publicação: 2014
Outros Autores: Torres, Ricardo Da S.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICIP.2014.7025379
http://hdl.handle.net/11449/168906
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 retrievalcorrelation graphunsupervised manifold learningThis 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.Department of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP)Recod Lab Institute of Computing University of Campinas (UNICAMP)Department of Statistics Applied Mathematics and Computing State University of São Paulo (UNESP)Universidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Pedronette, Daniel Carlos Guimaraes [UNESP]Torres, Ricardo Da S.2018-12-11T16:43:35Z2018-12-11T16:43:35Z2014-01-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1892-1896http://dx.doi.org/10.1109/ICIP.2014.70253792014 IEEE International Conference on Image Processing, ICIP 2014, p. 1892-1896.http://hdl.handle.net/11449/16890610.1109/ICIP.2014.70253792-s2.0-84983212530Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 IEEE International Conference on Image Processing, ICIP 2014info:eu-repo/semantics/openAccess2021-10-23T21:47:00Zoai:repositorio.unesp.br:11449/168906Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:06:55.975719Repositório 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
Pedronette, Daniel Carlos Guimaraes [UNESP]
content-based image retrieval
correlation graph
unsupervised manifold learning
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 Pedronette, Daniel Carlos Guimaraes [UNESP]
author_facet Pedronette, Daniel Carlos Guimaraes [UNESP]
Torres, Ricardo Da S.
author_role author
author2 Torres, Ricardo Da S.
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Estadual de Campinas (UNICAMP)
dc.contributor.author.fl_str_mv Pedronette, Daniel Carlos Guimaraes [UNESP]
Torres, Ricardo Da S.
dc.subject.por.fl_str_mv content-based image retrieval
correlation graph
unsupervised manifold learning
topic content-based image retrieval
correlation graph
unsupervised manifold learning
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 2014-01-28
2018-12-11T16:43:35Z
2018-12-11T16:43:35Z
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/10.1109/ICIP.2014.7025379
2014 IEEE International Conference on Image Processing, ICIP 2014, p. 1892-1896.
http://hdl.handle.net/11449/168906
10.1109/ICIP.2014.7025379
2-s2.0-84983212530
url http://dx.doi.org/10.1109/ICIP.2014.7025379
http://hdl.handle.net/11449/168906
identifier_str_mv 2014 IEEE International Conference on Image Processing, ICIP 2014, p. 1892-1896.
10.1109/ICIP.2014.7025379
2-s2.0-84983212530
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2014 IEEE International Conference on Image Processing, ICIP 2014
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.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
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