title: Bayesian texture classification based on contourlet transform and BYY harmony learning of Poisson mixtures.
abstract: As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on the adaptive model-selection learning of Poisson mixtures on the contourlet features of texture images. The adaptive model-selection learning of Poisson mixtures is carried out by the recently established adaptive gradient Bayesian Ying-Yang harmony learning algorithm for Poisson mixtures. It is demonstrated by the experiments that our proposed Bayesian classifier significantly improves the texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.
authors.affiliation: Department of Information Science, School of Mathematical Sciences, Peking University, Beijing, China.
authors.forename: Yongsheng
authors.initials: Y
authors.lastname: Dong
authors.forename: Jinwen
authors.initials: J
authors.lastname: Ma