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

Authors

Jiazhao Wang, University of WollongongFollow
Golshah Naghdy, University of WollongongFollow
Philip Ogunbona, University of WollongongFollow

RIS ID

65866

Publication Details

Wang, J., Naghdy, G. & Ogunbona, P. (1997). Wavelet-based feature-adaptive adaptive resonance theory neural network for texture identification. Journal of Electronic Imaging, 6 (3), 329-335.

Abstract

A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction 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 ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ 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 obtained 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.