Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks

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dc.contributor.authorPedronette, Daniel Carlos Guimarães [UNESP]
dc.contributor.authorGonçalves, Filipe Marcel Fernandes [UNESP]
dc.contributor.authorGuilherme, Ivan Rizzo [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:12:13Z
dc.date.available2018-12-11T17:12:13Z
dc.date.issued2018-03-01
dc.description.abstractPerforming effective image retrieval tasks, capable of exploiting the underlying structure of datasets still constitutes a challenge research scenario. This paper proposes a novel manifold learning approach that exploits the intrinsic dataset geometry for improving the effectiveness of image retrieval tasks. The underlying dataset manifold is modeled and analyzed in terms of a Reciprocal kNN Graph and its Connected Components. The method computes the new retrieval results on an unsupervised way, without the need of any user intervention. A large experimental evaluation was conducted, considering different image retrieval tasks, various datasets and features. The proposed method yields better effectiveness results than various methods recently proposed, achieving effectiveness gains up to +40.75%.en
dc.description.affiliationDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Rio Claro
dc.description.affiliationUnespDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), Rio Claro
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2013/08645-0
dc.format.extent161-174
dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2017.05.009
dc.identifier.citationPattern Recognition, v. 75, p. 161-174.
dc.identifier.doi10.1016/j.patcog.2017.05.009
dc.identifier.file2-s2.0-85019866884.pdf
dc.identifier.issn0031-3203
dc.identifier.scopus2-s2.0-85019866884
dc.identifier.urihttp://hdl.handle.net/11449/174645
dc.language.isoeng
dc.relation.ispartofPattern Recognition
dc.relation.ispartofsjr1,065
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectConnected components
dc.subjectContent-based image retrieval
dc.subjectReciprocal kNN graph
dc.subjectUnsupervised manifold learning
dc.titleUnsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasksen
dc.typeArtigo

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE