Learning Texture Discrimination Rules in a Multiresolution System

Abstract

We describe a texture analysis system in which informative discrimination rules are learned from a multiresolution representation of the 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. Index terms: Texture recognition; classification; segmentation; oriented pyramid; unsupervised clustering; learning; rule-based network; pattern-discovery. 1 Dr. Anderson is currently with the Department of Anatomy and Neuro..