Learning texture discrimination rules in a multiresolution system
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.
© 1994 IEEE. Manuscript received August 1, 1992; revised November 30, 1993. This work was supported in part by Pacific Bell, and in part by ARPA and ONR under grant no, N00014-92-J-1860. H. Greenspan was supported in part by an Intel fellowship. This research was carried out in part by the Jet Propulsion Laboratories, California Institute of Technology. Professor R. Chellappa's work was supported in part by an NSF Grant MIP 91-00655.
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