Bayesian learning of finite generalized Gaussian mixture models on images

This paper presents a fully Bayesian approach to analyze finite generalized Gaussian mixture models which incorporate several standard mixtures, widely used in signal and image processing applications, such as Laplace and Gaussian. Our work is motivated by the fact that the generalized Gaussian distribution (GGD) can be applied on a wide range of data due to its shape flexibility which justifies its usefulness to model the statistical behavior of multimedia signals [1]. We present a method to evaluate the posterior distribution and Bayes estimators using a Gibbs sampling algorithm. For the selection of number of components in the mixture, we use the integrated likelihood and Bayesian information criteria. We validate the proposed method by applying it to: synthetic data, real datasets, texture classification and retrieval, and image segmentation; while comparing it to different other approaches.