Best-Buddies Similarity—Robust Template Matching Using Mutual Nearest Neighbors
Name
BBS_CVPR15.pdf
Description
Accepted version
Size
10.4 MB
Format
Adobe PDF
Checksum (MD5)
7666486c49ea36394f5a188669b3d37a
Author(s)
Oron, Shaul
Dekel, Tali
Xue, Tianfan
Freeman, William T.
Avidan, Shai
Date Issued
August 1, 2018
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tpami.2017.2737424
