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2007
IEEE

Improving Descriptors for Fast Tree Matching by Optimal Linear Projection

9 years 7 months ago
Improving Descriptors for Fast Tree Matching by Optimal Linear Projection
In this paper we propose to transform an image descriptor so that nearest neighbor (NN) search for correspondences becomes the optimal matching strategy under the assumption that inter-image deviations of corresponding descriptors have Gaussian distribution. The Euclidean NN in the transformed domain corresponds to the NN according to a truncated Mahalanobis metric in the original descriptor space. We provide theoretical justification for the proposed approach and show experimentally that the transformation allows a significant dimensionality reduction and improves matching performance of a state-of-the art SIFT descriptor. We observe consistent improvement in precision-recall and speed of fast matching in tree structures at the expense of little overhead for projecting the descriptors into transformed space. In the context of SIFT vs. transformed MSIFT comparison, tree search structures are evaluated according to different criteria and query types. All search tree experiments confirm...
Krystian Mikolajczyk, Jiri Matas
Added 14 Oct 2009
Updated 14 Oct 2009
Type Conference
Year 2007
Where ICCV
Authors Krystian Mikolajczyk, Jiri Matas
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