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

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorGuimaraes Pedronette, Daniel Carlos-
Autor(es): dc.creatorTorres, Ricardo da S.-
Autor(es): dc.creatorIEEE-
Data de aceite: dc.date.accessioned2021-03-11T01:13:03Z-
Data de disponibilização: dc.date.available2021-03-11T01:13:03Z-
Data de envio: dc.date.issued2019-10-04-
Data de envio: dc.date.issued2019-10-04-
Data de envio: dc.date.issued2014-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/184782-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/184782-
Descrição: dc.descriptionThis 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.-
Formato: dc.format1892-1896-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2014 Ieee International Conference On Image Processing (icip)-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectcontent-based image retrieval-
Palavras-chave: dc.subjectunsupervised anifold learning-
Palavras-chave: dc.subjectcorrelation graph-
Título: dc.titleUNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL-
Aparece nas coleções:Repositório Institucional - Unesp

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