Color Texture Image Retrieval Based on Copula Multivariate Modeling in the Shearlet Domain

TL;DR

This paper introduces a novel color texture image retrieval method using Shearlet domain modeling with Copula multivariate models, demonstrating improved accuracy and efficiency over existing techniques.

Contribution

It proposes a new framework combining Shearlet transform and Copula models for better dependency capturing in texture retrieval tasks.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Shows efficient retrieval time in feature extraction and matching.

Validates the effectiveness of Gaussian and non-Gaussian Copula models.

Abstract

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.