PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning graph matching
Tiberio Caetano, Julian McAuley, Li Cheng, Quoc Le and Alex Smola
IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 31, Number 6, pp. 1048-1058, 2009. ISSN 0162-8828

Abstract

As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many formulations of this problem can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility and a quadratic term encodes edge compatibility. The main research focus in this theme is about designing efficient algorithms for approximately solving the quadratic assignment problem since it is NP-hard. In this paper, we turn our attention to a different question: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: The training examples are pairs of graphs and the “labels” are matches between them. Our experimental results reveal that learning can substantially improve the performance of standard graph matching algorithms. In particular, we find that simple linear assignment with such a learning scheme outperforms Graduated Assignment with bistochastic normalization, a state-of-the-art quadratic assignment relaxation algorithm.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5548
Deposited By:Julian McAuley
Deposited On:25 February 2010