Binary Gabor pattern: An efficient and robust descriptor for texture classification

In this paper, we present a simple yet efficient and effective multi-resolution approach to gray-scale and rotation invariant texture classification. Given a texture image, we at first convolve it with J Gabor filters sharing the same parameters except the parameter of orientation. Then by binarizing the obtained responses, we can get J bits at each location. Then, each location can be assigned a unique integer, namely “rotation invariant binary Gabor pattern (BGP_ri)”, formed from J bits associated with it using some rule. The classification is based on the image´s histogram of its BGP_ris at multiple scales. Using BGP_ri, there is no need for a pre-training step to learn a texton dictionary, as required in methods based on clustering such as MR8. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of BGP_ri over the other state-of-the-art texture representation methods evaluated. The Matlab source codes are publicly available at http://sse.tongji.edu.cn/linzhang/IQA/BGP/BGP.htm.