Multiscale Image Segmentation Using Bayesian Optimum Statistical Estimation

The wavelet domain hidden Markov tree (WHMT) model can decompose the original image into a multiscale and multiband representation. In traditional methods, each WHMT model has to be trained with a single texture image, i.e., each texture is represented by a corresponding WHMT model. This method is memory consuming and do not work for unknown textures. More importantly, the model training of the wavelet domain hidden Markov tree does not take into consideration of the classification likelihood of the foreground and background observations in an optimum sense. In this paper, we develop a probabilistic approach to learn the a priori distribution of foreground objects and backgrounds of WHMT based on the Bayesian optimum statistical classifiers. Instead of computing the class labels of each pixel in the image, we only compute the likelihood of each pixel that belongs to foreground and background, which is then assigned to the classification likelihood of WHMT model. The robustness and accuracy of the proposed algorithm is demonstrate by using four real world horse image come from the benchmark of Weizmann Horse database.