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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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 31; H. 6; S. 1048 - 1058
Hauptverfasser: Caetano, T.S., McAuley, J.J., Li Cheng, Le, Q.V., Smola, A.J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.06.2009
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2009.28