Wavelet-Based Texture Retrieval using Independent Component Analysis

Abstract

In this paper, a novel approach to texture retrieval using independent component analysis (ICA) in wavelet domain is proposed. It is well recognized that the wavelet coefficients in different subbands are statistically correlated, resulting in the fact that the product of the marginal distributions of wavelet coefficients is not accurate enough to characterize the stochastic properties of texture images. To tackle this problem, we employ (ICA) in feature extraction to decorrelate the analysis coefficients in different subbands, followed by modeling the marginal distributions of the separated sources using generalized Gaussian density (GGD), and perform similarity measure based on the maximum likelihood criterion. It is demonstrated by simulation results on a database consisting of 1776 texture images that the proposed method improve the accuracy of texture image retrieval in terms of average retrieval rate, compared with the traditional method using GGD for feature extraction and Kullback-Leibler divergence for similarity measure.