Best-Buddies Similarity for Robust Template Matching
Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman
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 a count of Best Buddies Pairs (BBPs)--pairs of points in which each one is the nearest neighbor 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.
BibTeX
@inproceedings{cvpr2015_bestbuddiessimil,
title = {Best-Buddies Similarity for Robust Template Matching},
author = {Tali Dekel and Shaul Oron and Michael Rubinstein and Shai Avidan and William T. Freeman},
booktitle = {CVPR 2015},
year = {2015}
}