On capturing likelihood disparity for unsupervised image segmentation

In this paper, we study unsupervised Bayesian image segmentation approach which involves capturing model likelihood disparities among different texture features with respect to a global statistical model. Specifically, wavelet-domain hidden Markov models are used to characterize the global textural behavior of images in the wavelet-domain. Three clustering methods, i.e., the K-mean, a soft clustering and a multiscale clustering are studied to convert the unsupervised segmentation problem into the self-supervised process by identifying the reliable training samples. In particular, multiscale clustering involves multiple context models from different scales for context fusion. The simulation results on synthetic mosaics show that the proposed unsupervised segmentation algorithm can achieve high classification accuracy that is close to the supervised one.