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Bayesian Texture Classification Based on Contourlet Transform and BYY Harmony Learning of Poisson Mixtures

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2 Author(s)
Yongsheng Dong ; Department of Information Science, School of Mathematical Sciences, and the Laboratory of Mathematics and Applied Mathematics, Peking University, Beijing, China ; Jinwen Ma

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.

Published in:

IEEE Transactions on Image Processing  (Volume:21 ,  Issue: 3 )