Unsupervised image segmentation using Markov random field models

Abstract

We present two unsupervised segmentation algorithms based on hierarchical Markov random field models for segmenting both noisy images and textured images. Each algorithm finds the the most likely number of classes, their associated model parameters and generates a corresponding segmentation of the image into these classes. This is achieved according to the maximum a posteriori criterion. To facilitate this, an MCMC algorithm is formulated to allow the direct sampling of all the above parameters from the posterior distribution of the image. To allow the number of classes to be sampled, a reversible jump is incorporated into the Markov Chain. Experimental results are presented showing rapid convergence of the algorithm to accurate solutions.