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Learning Texture Discrimination Rules in a Multiresolution System
September 1994 (vol. 16 no. 9)
pp. 894-901

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.

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Index Terms:
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
Citation:
H. Greenspan, R. Goodman, R. Chellappa, C.H. Anderson, "Learning Texture Discrimination Rules in a Multiresolution System," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 9, pp. 894-901, Sept. 1994, doi:10.1109/34.310685
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