A study of contextual modeling and texture characterization for multiscale Bayesian segmentation

In this paper, we demonstrate that multiscale Bayesian image segmentation can be enhanced by improving both contextual modeling and statistical texture characterization. Firstly, we show a joint multi-context and multiscale approach to achieve more robust contextual modeling by using multiple context models. Secondly, we study statistical texture characterization using wavelet-domain hidden Markov models (HMMs), and in particular, we use an improved HMM, HMT-3S, to obtain more accurate multiscale texture characterization. Experimental results show that both of them play important roles in multiscale Bayesian segmentation.