Learning Graph Matching

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....

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Podrobná bibliografie
Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 31; číslo 6; s. 1048 - 1058
Hlavní autoři:	Caetano, T.S., McAuley, J.J., Li Cheng, Le, Q.V., Smola, A.J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.06.2009 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Application software Applied sciences Artificial Intelligence Biological and medical sciences Cameras Combinatorics Combinatorics. Ordered structures Computer science; control theory; systems Computer vision Data mining Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects Graph matching Graph theory Graphs Image databases Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Information retrieval. Graph Intelligence Learning Machine learning Mathematical analysis Mathematical models Mathematics Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Optimal matching Optimization Pattern matching Pattern recognition Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Reproducibility of Results Sciences and techniques of general use Sensitivity and Specificity Structured Estimation Studies Subtraction Technique Support Vector Machines Surveillance Theoretical computing Computer vision Statistical analysis support vector machines Graph theory learning Quadratic assignment Pattern recognition Optimization structured estimation Model matching NP hard problem Vector support machine Objective function Graph method Compatibility Pattern analysis Bioinformatics Artificial intelligence Pattern matching Graph matching
ISSN:	0162-8828, 1939-3539
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Popis
Shrnutí:	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 normalisation, a state-of-the-art quadratic assignment relaxation algorithm.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2009.28