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Learning texture discrimination rules in a multiresolution system

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4 Author(s)
Greenspan, H. ; Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA ; Goodman, R. ; Chellappa, R. ; Anderson, C.H.

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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:16 ,  Issue: 9 )