UNSUPERVISED MANIFOLD LEARNING BY CORRELATION GRAPH AND STRONGLY CONNECTED COMPONENTS FOR IMAGE RETRIEVAL
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Data
2014-01-01
Autores
Guimaraes Pedronette, Daniel Carlos [UNESP]
Torres, Ricardo da S.
IEEE
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Editor
Ieee
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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Palavras-chave
content-based image retrieval, unsupervised anifold learning, correlation graph
Como citar
2014 Ieee International Conference On Image Processing (icip). New York: Ieee, p. 1892-1896, 2014.