Texture image segmentation is the first essential and important step of low level vision. The extraction of texture features is a most fundamental problem to texture image segmentation. Many methods for extracting texture features of the image have been proposed, such as statistical features, co-occurrence features, two-dimensional AR features, and fractal based features etc. In this paper, a new method for extracting texture features of the image is proposed. In this method, the gray scale image is first decomposed into a series of binary images by variable thresholds, and then topological features of all of these binary images are computed. Using these topological features as texture features, we apply a pyramid linking with band-pass filter neural networks to segment the texture image into some homogeneous areas. Several experiments on synthetic texture images have been carried out to verify the efficacy of the new method.
