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<p>We describe a texture analysis system in which informative discrimination rules are learned from a multiresolution representation of time textured input. The system incorporates unsupervised and supervised learning via statistical machine learning and rule-based neural networks, respectively. The textured input is represented in the frequency-orientation space via a log-Gabor pyramidal decomposition. In the unsupervised learning stage a statistical clustering scheme is used for the quantization of the feature-vector attributes. A supervised stage follows in which labeling of the textured map is achieved using a rule-based network. Simulation results for the texture classification task are given. An application of the system to real-world problems is demonstrated.</p>
image texture; unsupervised learning; pattern recognition; neural nets; learning systems; knowledge based systems; computer vision; texture discrimination rule learning; multiresolution system; texture analysis system; informative discrimination rules; supervised learning; unsupervised learning; statistical machine learning; rule-based neural networks; frequency-orientation space; log-Gabor pyramidal decomposition; statistical clustering; quantization; feature-vector attributes; labeling; textured map; texture classification

C. Anderson, R. Goodman, H. Greenspan and R. Chellappa, "Learning Texture Discrimination Rules in a Multiresolution System," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 894-901, 1994.
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