Wavelet-based feature-adaptive adaptive resonance theory neural network for texture identification

Abstract

A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extrac- tion based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ''low- level'' features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ''cognitive sys- tem.'' The novelty of the model developed in this paper lies in the use of a self-organizing input layer to the fuzzy ART. Evaluation of the model is performed by using natural textures, and results ob- tained show that the developed model is capable of performing the texture recognition task effectively. Applications of the developed model include the study of artificial vision systems motivated by the human visual system model. © 1997 SPIE and IS&T. (S1017-9909(97)00403-0)