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

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2019-10-04T12:30:05Z
dc.date.accessioned2022-12-19T17:59:11Z
dc.date.available2019-10-04T12:30:05Z
dc.date.available2022-12-19T17:59:11Z
dc.date.created2019-10-04T12:30:05Z
dc.date.issued2014-01-01
dc.identifier2014 Ieee International Conference On Image Processing (icip). New York: Ieee, p. 1892-1896, 2014.
dc.identifier1522-4880
dc.identifierhttp://hdl.handle.net/11449/184782
dc.identifierWOS:000370063602013
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5365835
dc.description.abstractThis 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.
dc.languageeng
dc.publisherIeee
dc.relation2014 Ieee International Conference On Image Processing (icip)
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectcontent-based image retrieval
dc.subjectunsupervised anifold learning
dc.subjectcorrelation graph
dc.titleUNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
dc.typeActas de congresos


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