Best-Buddies Similarity for Robust Template Matching
Venue
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2015)
Publication Year
2015
Authors
Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman
BibTeX
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.
