  In this paper, a color texture image retrieval framework is proposed based on
Shearlet domain modeling using Copula multivariate model. In the proposed
framework, Gaussian Copula is used to model the dependencies between different
sub-bands of the Non Subsample Shearlet Transform (NSST) and non-Gaussian
models are used for marginal modeling of the coefficients. Six different
schemes are proposed for modeling NSST coefficients based on the four types of
neighboring defined; moreover, Kullback Leibler Divergence(KLD) close form is
calculated in different situations for the two Gaussian Copula and non Gaussian
functions in order to investigate the similarities in the proposed retrieval
framework. The Jeffery divergence (JD) criterion, which is a symmetrical
version of KLD, is used for investigating similarities in the proposed
framework. We have implemented our experiments on four texture image retrieval
benchmark datasets, the results of which show the superiority of the proposed
framework over the existing state-of-the-art methods. In addition, the
retrieval time of the proposed framework is also analyzed in the two steps of
feature extraction and similarity matching, which also shows that the proposed
framework enjoys an appropriate retrieval time.
