Contourlet-based texture classification with product bernoulli distributions

Authors:
Yongsheng Dong;Jinwen Ma
Affiliations:
Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China;Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

Citing 19
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Abstract

In this paper, we propose a novel texture classification method based on product Bernoulli distributions (PBD) and contourlet transform. In particular, product Bernoulli distributions (PBD) are employed for modeling the coefficients in each contourlet subband of a texture image. By investigating these bit-plane probabilities (BPs), we use the weighted L1-norm to discriminate the bit-plane probabilities of the corresponding subbands of two texture images and establish a new distance between the two images. Moreover, the K-nearest neighbor classifier is utilized to perform supervised texture classification. It is demonstrated by the experiments that our proposed method outperforms some current state-of-the-art approaches.