Texture classification using a fuzzy texture spectrum and neural networks

In the research areas in computer vision, many applications have been discovered using texture classification techniques, such as the content retrieval in multimedia, the computer-aided diagnosis 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 texture classification, which is based on the relative gray levels between pixels. A vector of fuzzy values is used to indicate the relationship of the gray levels between the neighboring pixels. The fuzzy texture spectrum can be considered as the distribution of the fuzzified 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%. (C) 1998 SPIE and IS&T. [S1017-9909(98)00301-8].