Evaluation of local detectors and descriptors for fast feature matching

Published on Nov 1, 2012 in ICPR (International Conference on Pattern Recognition)
Ondrej Miksik17
Estimated H-index: 17
,
Krystian Mikolajczyk47
Estimated H-index: 47
Sources
Abstract
Local feature detectors and descriptors are widely used in many computer vision applications and various methods have been proposed during the past decade. There have been a number of evaluations focused on various aspects of local features, matching accuracy in particular, however there has been no comparisons considering the accuracy and speed trade-offs of recent extractors such as BRIEF, BRISK, ORB, MRRID, MROGH and LIOP. This paper provides a performance evaluation of recent feature detectors and compares their matching precision and speed in randomized kd-trees setup as well as an evaluation of binary descriptors with efficient computation of Hamming distance.
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