A wavelet domain hierarchical hidden Markov model

This paper proposes a wavelet-domain hierarchical hidden Markov model for an unsupervised texture segmentation. Based on a hybrid graph structure, the global dependencies can be captured by a quad-tree structure across all scales, and local dependencies at higher resolution scales can be captured by a pyramidal graph structure. A novel context model that includes different positions, orientations, and scales is introduced. Applications of an unsupervised texture segmentation are presented. Compared with other alternative approaches for several test images, this method can achieve a significant improvement in segmentation, especially at higher resolution scales.