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Adaptive Gray Level Co-occurrence Probabilities for Texture Segmentation Based on Coarse-to-fine Strategy

In the applications of image segmentation, the tasks usually focused on extracting the exact boundaries among different texture classes. Traditionally, the popular texture feature extraction approach, gray level cooccurrence probability (GLCP) method, is widely used for extracting the boundaries. It scans the whole image by windows with fixed size to count the gray level co-occurrence and uses statistics to calculate the texture characteristics at each window. However, it has a tendency to misclassify and erode texture boundaries, especially for large window sizes and irregular texture boundaries in images. This paper presents the adaptive gray level co-occurrence probability (AGLCP) approach to improve the original GLCP method for the analysis of the image textures. Based on the coarse-to-fine strategy, the AGLCP method can adjust the scanning window and analyze texture features, adaptively. Therefore, it can generate a more suitable statistic characteristic matrix for further image segmentation. Experimental results show that this method can preserve the edge strength between textures and provide better segmentations when comparing with the previous methods.