Bayesian color image segmentation using reversible jump Markov chain Monte Carlo

This paper deals with the problem of unsupervised image segmentation. Our goal is to propose a method which is able to segment a color image without any human intervention. The only input is the observed image, all other parameters are estimated during the segmentation process. Our method is model-based, we use a first order Markov random field (MRF) model (also known as the Potts model) where the singleton energies derive from a multivariate Gaussian distribution and second order potentials favor similar classes in neighboring pixels. The most difficult part is the estimation of the number of pixel classes or in other words, the estimation of the number of Gaussian mixture components. Reversible jump Markov chain Monte Carlo (MCMC) is used to solve this problem. These jumps enable the possible splitting and merging of classes. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The estimation is done according to the Maximum A Posteriori (MAP) criteria. Experimental results are promising, we have obtained accurate results on a variety of real color images.