Abstract-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 wellknown databases show significant improvements in retrieval rates using the proposed method compared with the best known stateof-the-art approaches.
In literature REF Lasmar and Berthoumieu use Gaussian copula to fit wavelet coefficients and they derived a closed-form Kullback-Leibler Divergence (KLD) as the similarity measure between Gaussian copula.
Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Computer Science Mathematics Medicine