Binary feature from intensity quantization and weakly spatial contextual coding for image search

During the past few years, a number of local binary feature descriptors for images have been proposed, e.g. BRIEF, ORB, BRISK, and FREAK. The binary descriptors have several advantages over the well-established floating vector descriptors such as SIFT and SURF, for their fast computing speed and much low memory consumption. Nevertheless, the binary descriptor still suffer from the poor performance in computer vision applications for its low discriminative power and Hamming distance metric. To improve the capability of binary descriptors, some works focusing on the points selection pattern algorithm in the descriptor extraction are proposed. These works mostly adopt more optimal selection pattern (BRISK and FREAK) to enhance the performance of binary descriptors, rather than random chosen pattern. In our study, however, the points selection algorithm does not have much contributions for the promotion on performance. Therefore, in this paper, we try to solve the problem of low discriminative power and robustness through two novel methods: Intensity Difference Quantization and Weakly Spatial Context Coding. The experimental results on the public datasets show that our method can significantly boost the performance of binary features and highly enhance the retrieval accuracy of the image search system, even though our proposed method increases slightly memory usage and computing efficiency, compared to the original binary descriptors.