Texture retrieval based on multivariate Log-Gaussian mixture model

Abstract

This paper proposed an efficient texture retrieval method for indexing images with heavy-tailed distribution, such as biomedical images, sonar images and natural texture images. In the proposed scheme, a multivariate Log-Gaussian mixture model (MLGMM) was used to model the sharp peaks, heavy tails, and even the multimodal statistical properties of two-dimension Gabor coefficients of texture under different scales and orientations. The parameters of MLGMM are estimated by expectation maximum (EM). In our scheme, each class of texture is modeled by one MLGMM and Bayesian classification is implemented by feeding the output of MMLGM into the Bayesian classifier. Experiments on feature extraction and similarity measurement have been done to demonstrate the effectiveness of our proposed algorithm. Extensive experiments have validated that our retrieval scheme has an average retrieval rate of 2% higher than other related texture statistical techniques.