Supervised Texture Classification Using Characteristic Generalized Gaussian Density
Journal of Mathematical Imaging and Vision
Intelligent Processing of Medical Images in the Wavelet Domain
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
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
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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This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images. Our signatures have smaller storage requirement and lower computational complexity, and yet, experimental results on texture image retrieval show that our proposed signatures are much more cost effective to current state-of-the-art methods including the generalized Gaussian density signatures and histogram signatures