MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching

• Published in: PloS one Vol. 12; no. 5; p. e0178090
• Main Authors: Su, Mingzhe, Ma, Yan, Zhang, Xiangfen, Wang, Yan, Zhang, Yuping
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.05.2017; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Binary digits; Biology and Life Sciences; Brightness; Coding; Computer and Information Sciences; Computer simulation; Computer vision; Data processing; Digits; Distortion; Electrical engineering; Engineering; Engineering and Technology; Euclidean geometry; Exploration; Format; Horizontal cells; Image coding; Image databases; Image Interpretation, Computer-Assisted - methods; Image processing; Image retrieval; Intelligence; International conferences; Mathematical models; Mean square values; Multimedia; Object recognition; Pattern recognition; Pattern Recognition, Automated; Photometry; Physical Sciences; Reflection; Research and Analysis Methods; Retina; Robustness (mathematics); Scaling; Segmentation; Social Sciences; Strings; Three dimensional models; Visual communication
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0178090

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these problems, in this paper, we present a horizontal or vertical mirror reflection invariant binary descriptor named MBR-SIFT, in addition to a novel image matching approach. First, 16 cells in the local region around the SIFT keypoint are reorganized, and then the 128-dimensional vector of the SIFT descriptor is transformed into a reconstructed vector according to eight directions. Finally, the MBR-SIFT descriptor is obtained after binarization and reverse coding. To improve the matching speed and accuracy, a fast matching algorithm that includes a coarse-to-fine two-step matching strategy in addition to two similarity measures for the MBR-SIFT descriptor are proposed. Experimental results on the UKBench dataset show that the proposed method not only solves the problem of mirror reflection, but also ensures desirable matching accuracy and speed.