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 L 1-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.
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Dong, Y., Ma, J. (2011). Contourlet-Based Texture Classification with Product Bernoulli Distributions. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_2
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DOI: https://doi.org/10.1007/978-3-642-21090-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21089-1
Online ISBN: 978-3-642-21090-7
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