Best-Buddies Similarity—Robust Template Matching Using Mutual Nearest Neighbors

@article{Oron2018BestBuddiesST,
  title={Best-Buddies Similarity—Robust Template Matching Using Mutual Nearest Neighbors},
  author={Shaul Oron and Tali Dekel and Tianfan Xue and William T. Freeman and Shai Avidan},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2018},
  volume={40},
  pages={1799-1813}
}
  • Shaul Oron, Tali Dekel, S. Avidan
  • Published 6 September 2016
  • Computer Science
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
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… 
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48 Citations
Highly Influential Citations
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48 Citations

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References

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