New wavelet based ART network for texture classification

Authors

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

RIS ID

65868

Publication Details

Wang, J., Naghdy, G. & Ogunbona, P. O. (1996). New wavelet based ART network for texture classification. Proceedings of the Australian and New Zealand Conference on Intelligent Information Systems (pp. 250-253). IEEE.

Abstract

A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory (Fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the Fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using natural textures. The results obtained show that it is able to effectively perform the object recognition task and will find useful application in the study of the human visual system model for artificial vision.