PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient high order matching
Yosi Keller and Michael Chertok
IEEE Transactions on Pattern Analysis and Machine Intelligence 2009.

Abstract

We present a computational approach to high order matching of datasets in Rd. Those are matchings based on data affinity measures that score the matching of more than two pairs of points at a time. High order affinities are represented by tensors and the matching is then given by a rank-one approximation of the affinity tensor and a corresponding discretization. Our approach is rigorously justified by extending Zass and Shashua’s hypergraph matching [40] to high order spectral matching. This paves the way for a computationally efficient dual marginalization-spectral matching scheme. We also show that based on the spectral properties of random matrices, affinity tensors can be randomly sparsified while retaining the matching accuracy. Our contributions are experimentally validated by applying them to synthetic as well as real datasets

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:5831
Deposited By:Yosi Keller
Deposited On:08 March 2010