Pairwise Cross Pattern: A Color-LBP Descriptor for Content-Based Image Retrieval

The local binary pattern (LBP) has been widely considered an excellent and extensive feature descriptor, but it is limited to gray-scale image processing. Inspired by human visual system, we develop a novel yet simple rotation-invariant color-LBP descriptor—pairwise cross pattern (PCP) to extend LBP to color image processing. In the proposed descriptor, the color information map is firstly extracted using a multi-level color quantizer which is designed based on a color distribution prior in the L*a*b* color space. Then, the color information and LBP maps are paired in parallel to construct a pairwise cross pattern, which is easily extended to the uniform pairwise cross pattern (UPCP) and the rotation-invariant pairwise cross pattern (RIPCP). Finally, compared to numerous state-of-the-art schemes and convolutional neural network (CNN)-based models, the experimental results illustrate that the proposed method is efficient, effective and robust in content-based image retrieval task.