EFFICIENT PROPOSAL DISTRIBUTIONS FOR MCMC IMAGE SEGMENTATION

We present methods to obtain computationally efficient proposal distributions for Bayesian reversible jump Markov chain Monte Carlo (RJMCMC) based image segmentation. The slow convergence of MCMC methods often makes them poorly suited for practical image processing applications. We show how carefully crafted proposal distributions along with certain approximations can decrease the computational cost of MCMC image segmentation to a level that is comparable with some traditional algorithms. We also discuss the interpretation of the resulting distribution of different segmentations and present experimental results. 1