A Robust and Compact Descriptor Based on Center-Symmetric LBP
- Resource Type
- Conference
- Authors
- Xiao, Jinwei; Wu, Gangshan
- Source
- 2011 Sixth International Conference on Image and Graphics Image and Graphics (ICIG), 2011 Sixth International Conference on. :388-393 Aug, 2011
- Subject
- Computing and Processing
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Principal component analysis
Noise
Detectors
Histograms
Face recognition
Face
- Language
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.