Texture classification using a fuzzy texture spectrum and neural networks

Abstract

In the research areas in computer vision, many applica- tions have been discovered using texture classification techniques, such as the content retrieval in multimedia, the computer-aided di- agnosis of medical images, and the segmentation of remote sensing images. The success of the texture classification of a given set of images hinges on the designs of texture features and the classifiers. We present a new texture feature, fuzzy texture spectrum, for tex- ture classification, which is based on the relative gray levels be- tween pixels. A vector of fuzzy values is used to indicate the rela- tionship of the gray levels between the neighboring pixels. The fuzzy texture spectrum can be considered as the distribution of the fuzzi- fied differences between the neighboring pixels. It is an improved version of the reduced texture spectrum, and it is less sensitive to the noise and the changing of the background brightness in texture images. We use 12 Brodatz texture images in the simulations to show the effectiveness of the new texture feature. Our simulation results show that the rate of classification error can be reduced to 0.2083%. © 1998 SPIE and IS&T. (S1017-9909(98)00301-8)