Fuzzy ARTMAP based classification technique of natural textures

Abstract

This paper describes an approach to classification of textured grayscale images using a technique based on image filtering and the fractal dimension (FD) and the Fuzzy ARTMAP neural network (FAMNN). Twelve FD features are computed based on twelve filtered versions of the original image using directional Gabor filters. Features are computed in a window and mapped to the central pixel of this window. We implemented a variation of the testing phase of Fuzzy ARTMAP that exhibited superior performance than the standard Fuzzy ARTMAP and the 1-nearest neighbor (1-NN) in the presence of noise. Training was performed using patterns that were extracted from twenty different textures. The performance of classification is also studied with respect to a testing set. Segmentation results are also presented to illustrate that the classification algorithm and its specified parameters are adequate so that more than one texture can be identified in the same image.