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A general framework for image feature matching without geometric constraints.
- Source :
-
Pattern Recognition Letters . Apr2016, Vol. 73, p26-32. 7p. - Publication Year :
- 2016
-
Abstract
- Computer vision applications that involve the matching of local image features frequently use Ratio-Match as introduced by Lowe and others, but is this really the optimal approach? We formalize the theoretical foundation of Ratio-Match and propose a general framework encompassing Ratio-Match and three other matching methods. Using this framework, we establish a theoretical performance ranking in terms of precision and recall, proving that all three methods consistently outperform or equal Ratio-Match . We confirm the theoretical results experimentally on over 3000 image pairs and show that matching precision can be increased by up to 20 percentage-points without further assumptions about the images we are using. These gains are achieved by making only a few key changes of the Ratio-Match algorithm that do not affect computation times. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 73
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 113951511
- Full Text :
- https://doi.org/10.1016/j.patrec.2015.12.017