A Robust and Compact Descriptor Based on Center-Symmetric LBP

Center-symmetric local binary pattern (CS-LBP) is a novel texture feature which utilizes texture to describe the local regions. It combines the good property of local binary pattern (LBP) and SIFT. It has been extended to a region descriptor and achieved promising performance in many applications. However, it is sensitive to noise and less efficient due to its high dimensional descriptor vector. Due to these, we propose a novel descriptor based on CS-LBP operator denoted as PCA-CS-LBP. Our proposed descriptor achieves better noise robustness using the difference of pixels instead of the rough comparing of pixels. Besides, PCA is employed and applied to generate a more compact representation. Comparisons between our descriptor and standard CS-LBP descriptor are given on a standard image matching dataset. Experimental results show that our descriptor is outperforms the standard CS-LBP descriptor in most cases.