Bayesian image segmentation based on an inhomogenous hidden Markov random field
Sun Junxi, Dongbing Gu · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · 2004
This paper introduces a Bayesian image segmentation algorithm with the consideration of label scale variability in many images. An inhomogeneous hidden Markov random field is adopted in this algorithm to model the label scale variability as a prior probability. An EM algorithm is developed to estimate parameters for both the prior probability and likelihood probability. The image segmentation is established by a MAP estimator. Different images are tested to verify our algorithm. Comparisons with other segmentation algorithms are made. The segmentation results show that our algorithm has better performance than others.