Training spiking neural networks with the improved grey-level co-occurrence matrix algorithm for texture analysis

摘要

Texture refers to the tactile impression, such as rough, silky, bumpy, and other texture terms. The Grey-Level Co-occurrence Matrix (GLCM) algorithm is widely used in visual images for texture feature extraction, image structure characterization analysis and texture classification. The GLCM can not only give the statistics of pixel gray values occur in an image, but also give multiple characteristics of the images. Since the primate brain, which is constructed with spiking neurons, has excellent performance in terms of image feature extraction, the improved GLCM algorithm is used to train a spiking neural network and also to simulate the brain's ability about extract key information and utilize these extracted feature information to classify different texture image. Experimental results in this article show that this combination of the GLCM and spiking neural network can effectively extract image features, and the texture classification results is also to achieve satisfactory effect.