IEEE Transactions on Image Processing
Texture classification using refined histogram
IEEE Transactions on Image Processing
Watermark detection on quantized transform coefficients using product bernoulli distributions
Proceedings of the 12th ACM workshop on Multimedia and security
Contourlet-based texture classification with product bernoulli distributions
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Texture classification based on contourlet subband clustering
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Statistical contourlet subband characterization for texture image retrieval
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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The modeling of wavelet subband histograms via the product Bernoulli distributions (PBD) has received a lot of interest and the PBD model has been applied successfully in texture image retrieval. In order to fully understand the usefulness and effectiveness of the PBD model and its associated signature, namely, the bit-plane probability (BP) signature on image processing applications, we discuss and investigate some of their statistical properties. These properties would help to clarify the sufficiency of the BP signature to characterize wavelet subbands, which, in turn, justifies its use in real time applications. We apply the BP signature on supervised texture classification problem and experimental results suggest that the weighted L1-norm (rather than the standard L1-norm) should be used for the BP signature. Comparative classification experiments show that our method outperforms the current state-of-the-art Generalized Gaussian Density approaches.