Best-Buddies Similarity - Robust Template Matching using Mutual Nearest
Neighbors
release_s7yu4h3qpzgxbklaf533fe6iva
by
Shaul Oron, Tali Dekel, Tianfan Xue, William T. Freeman, Shai Avidan
2016
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, where each point 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 while using different types
of features.
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