Features Combined Binary Descriptor Based on Voted Ring-Sampling Pattern

Hongmin Liu, Qianqian Zhang, Bin Fan, Zhiheng Wang, Junwei Han

Abstract: Most existing binary descriptors are only based on intensity and simply compare averaged intensities of one sample pair to obtain binary output. To address this issue, we propose a novel ring-sampling pattern based binary descriptor encoding both intensity and gradient. For intensity coding, a ring-sampling pattern is presented to define a number of sample points and their neighboring points given an interest point. The intensity difference between two sample points is obtained by performing the comparisons of the intensities of their corresponding neighboring points directly. Further, a majority based voting strategy is employed to obtain compact representation (1 bit or 3 bits) based on all these intensity difference among neighboring points. As for gradient coding, the gradient orientation histogram is computed for each sample point, and the gradient difference of two sample points is obtained by comparing the gradient magnitude on each orientation bin. The raw binary descriptor is constructed by concentrating the intensity and gradient differences of all the sample pairs, and two feature selection strategies are proposed to obtain the final compact descriptor, named as Features Combined Binary Descriptor based on Voted Ring-Sampling Pattern (BDVRP). The experimental results on the tasks of object recognition and image matching demonstrate the superiority and the effectiveness of the proposed descriptor, with comparison to the state-of-the-art hand-crafted binary descriptors.