BYY harmony learning of t-mixtures with the application to image segmentation based on contourlet texture features

In this paper, we extend Bayesian Ying-Yang (BYY) harmony learning to the case of multivariate t-mixtures and propose a gradient BYY harmony learning algorithm that can automatically determine the number of actual t-distributions in a dataset during parameter learning. It is demonstrated by simulation experiments that this proposed algorithm for t-mixtures is both effective and stable on model selection and parameter estimation. Moreover, by mainly utilizing certain contourlet texture features from an image, the proposed algorithm is successfully applied to unsupervised image segmentation, showing considerable advantages for both general and multi-texture images.