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Match-time covariance for descriptors (2013)
BibTeX
@MISC{Christiansen13match-timecovariance,
author = {Eric Christiansen and Vincent Rabaud and Andrew Ziegler and David Kriegman and Serge Belongie},
title = { Match-time covariance for descriptors},
year = {2013}
}
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Abstract
Local descriptor methods are widely used in computer vision to compare local regions of images. These descriptors are often extracted relative to an estimated scale and rotation to provide invariance up to similarity transformations. The estimation of rotation and scale in local neighborhoods (also known as steering) is an imperfect process, however, and can produce errors downstream. In this paper, we propose an alter-native to steering that we refer to as match-time covariance (MTC). MTC is a general strategy for descriptor design that simultaneously provides invariance in local neighbor-hood matches together with the associated aligning transformations. We also provide a general framework for endowing existing descriptors with similarity invariance through MTC. The framework, Similarity-MTC, is simple and dramatically improves accuracy. Finally, we propose NCC-S, a highly effective descriptor based on classic normalized cross-correlation, designed for fast execution in the Similarity-MTC framework. The surprising effectiveness of this very simple descriptor suggests that MTC offers fruitful research directions for image matching previously not accessible in the steering based paradigm.
Keyphrases
match-time covariance match-time covariance descriptor simple descriptor general framework similarity invariance fruitful research direction similarity-mtc framework local neighborhood computer vision associated aligning transformation imperfect pro-cess surprising effectiveness classic normalized cross-correlation fast execution effective descriptor similarity transformation local descriptor method descriptor design general strategy local neighbor-hood match local re-gions
