Best-Buddies Similarity—Robust Template Matching Using Mutual Nearest Neighbors
Author(s)Oron, Shaul; Dekel, Tali; Xue, Tianfan; Freeman, William T.; Avidan, Shai
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We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs) - pairs of points in source and target sets that are mutual nearest neighbours, i.e., each point is the nearest neighbour of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset while using different types of features.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
IEEE Transactions on Pattern Analysis and Machine Intelligence
Institute of Electrical and Electronics Engineers (IEEE)
Oron, Shaul, et al. “Best-Buddies Similarity—Robust Template Matching Using Mutual Nearest Neighbors.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 8 (August 2018): 1799–813. © 2017 IEEE.
Author's final manuscript