Binary Gabor pattern: An efficient and robust descriptor for texture classification
Lin Zhang, Zhiqiang Zhou, Hongyu Li · 2012
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 (BGPri)”, formed from J bits associated with it using some rule. The classification is based on the image's histogram of its BGPris at multiple scales. Using BGPri, 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 BGPriover 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.