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• W4380342443 abstract "This work proposes a novel method for color-textured image retrieval on a Multivariate Generalized Gamma Distribution manifold (MGΓD). Thanks to the Gaussian copula theory, we define the expression of MGΓD, which efficiently models the statistical dependence structure between dual-tree complex wavelet transform (DTCWT) of the color components. The major contribution of this paper is to provide a geometric perspective to the MGΓD by treating it as a Riemannian manifold while proposing the geodesic distance (GD) as a measure of Riemannian similarity on it. Based on information geometry tools, we conduct a geometrical study of the MGΓD manifold, allowing us to derive two suitable approximations of the GD. The experiments are performed on five well-known color texture databases, considering the content-based image retrieval (CBIR) framework and using the RGB color space. The obtained results demonstrate the efficiency of the geometric interpretation through the proposed GD as a natural and intuitive similarity measure on the studied statistical manifold." @default.
• W4380342443 created "2023-06-13" @default.
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• W4380342443 date "2023-11-01" @default.
• W4380342443 modified "2023-09-26" @default.
• W4380342443 title "Exploring multivariate generalized gamma manifold for color texture retrieval" @default.
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• W4380342443 doi "https://doi.org/10.1016/j.patcog.2023.109748" @default.
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