The Ising/Potts model is not well suited to segmentation tasks

Abstract

The Ising and Potts models have been used since the earliest work on Markov random fields (MRF) based image segmentation as the underlying model for the region labels, and continue to be used for this task. However, advances in Markov chain Monte Carlo techniques have highlighted the shortcomings of these models as models of region labels. We present a demonstration of why these models are unsuitable for segmentation. We hope this will help motivate the search for better models.