Best-Buddies Similarity - Robust Template Matching using Mutual Nearest Neighbors release_s7yu4h3qpzgxbklaf533fe6iva

by Shaul Oron, Tali Dekel, Tianfan Xue, William T. Freeman, Shai Avidan

Released as a article .

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.
In text/plain format

Archived Files and Locations

application/pdf  3.3 MB
file_iwooy3fptrhzdpux3v5kkazvoa
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2016-09-06
Version   v1
Language   en ?
arXiv  1609.01571v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: ce853753-db4d-4ee4-a1fe-43a569e4918f
API URL: JSON