CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.06  June
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Issue No.06  June (2009 vol.31)
pp: 10481058
Julian J. McAuley , NICTA and Australian National University, Australia
Li Cheng , TTIChicago, Chicago
Quoc V. Le , Stanford University, Stanford
Tibério S. Caetano , NICTA and Australian National University, Australia
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.28
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 NPhard. 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 normalisation, a stateoftheart quadratic assignment relaxation algorithm.
INDEX TERMS
Graph matching, Learning, Support Vector Machines, Structured Estimation, Optimization
CITATION
Julian J. McAuley, Li Cheng, Quoc V. Le, Tibério S. Caetano, "Learning Graph Matching", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 6, pp. 10481058, June 2009, doi:10.1109/TPAMI.2009.28
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