Multivariate statistical modeling for texture analysis using wavelet transforms

In the framework of wavelet-based analysis, this paper deals with texture modeling for classification or retrieval systems using non-Gaussian multivariate statistical features. We propose a stochastic model based on Spherically Invariant Random Vectors (SIRVs) joint density function with Weibull assumption to characterize the dependences between wavelet coefficients. For measuring similarity between two texture images, the Kullback-Leibler divergence (KLD) between the corresponding joint distributions is provided. The evaluation of model performance is carried out in the framework of retrieval system in terms of recognition rate. A comparative study between the proposed model and conventional models such as univariate Generalized Gaussian distribution and Multivariate Bessel K forms (MBKF) is conducted.