Sampled Multi-scale Color Local Binary Patterns

In this paper, we propose a novel representation, called sampled multi-scale color Local Binary Pattern (SMC-LBP), and apply it to Visual Object Classes (VOC) Recognition. The Local Binary Pattern (LBP) has been proven to be effective for image representation, but it is too local to be robust. Meanwhile such a design cannot fully exploit the discriminative capacity of the features available and deal with various changes in lighting and viewing conditions in real-world scenes. In order to address these problems, we propose SMC-LBP, which randomly samples the neighboring pixels across different scale circles, instead of pixels from individual circular in the original LBP scheme. The proposed descriptor presents several advantages: (1) It encodes not only single scale but also multiple scales of image patterns, and hence provides a more complete image information than the original LBP descriptor; (2) It cooperates with color information, therefore its photometric invariance property and discriminative power is enhanced. The experimental results on the PASCAL VOC 2007 image benchmark show significant accuracy improvement by the proposed descriptor compared with both the original LBP and other popular texture descriptors.