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Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms
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DOI：10.1109/TIP.2014.2313232.png)
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Abstract
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In the framework of texture image retrieval, a new family of stochastic multivariate modeling is proposed based on Gaussian Copula and wavelet decompositions. We take advantage of the copula paradigm, which makes it possible to separate dependence structure from marginal behavior. We introduce two new multivariate models using, respectively, generalized Gaussian and Weibull densities. These models capture both the subband marginal distributions and the correlation between wavelet coefficients. We derive, as a similarity measure, a closed form expression of the Jeffrey divergence between Gaussian copula-based multivariate models. Experimental results on well-known databases show significant improvements in retrieval rates using the proposed method compared with the best known state-of-the-art approaches.
Keywords:
Texture
Gaussian copula
multivariate generalized Gaussian
multivariate Weibull
Jeffrey divergence
wavelet transforms
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13.7
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