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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| Vydáno v: | IEEE transactions on pattern analysis and machine intelligence Ročník 31; číslo 6; s. 1048 - 1058 |
|---|---|
| Hlavní autoři: | , , , , |
| 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: | |
| ISSN: | 0162-8828, 1939-3539 |
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| 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 normalisation, a state-of-the-art quadratic assignment relaxation algorithm. |
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| AbstractList | 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 [abstract truncated by publisher]. 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. 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. 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.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. |
| Author | Caetano, T.S. McAuley, J.J. Li Cheng Smola, A.J. Le, Q.V. |
| Author_xml | – sequence: 1 givenname: T.S. surname: Caetano fullname: Caetano, T.S. organization: Stat. Machine Learning Group, NICTA, Canberra, ACT – sequence: 2 givenname: J.J. surname: McAuley fullname: McAuley, J.J. organization: Stat. Machine Learning Group, NICTA, Canberra, ACT – sequence: 3 surname: Li Cheng fullname: Li Cheng organization: TTI-Chicago, Chicago, IL – sequence: 4 givenname: Q.V. surname: Le fullname: Le, Q.V. organization: Dept. of Comput. Sci., Stanford Univ., Stanford, CA – sequence: 5 givenname: A.J. surname: Smola fullname: Smola, A.J. organization: Yahoo! Res., Santa Clara, CA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21501459$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/19372609$$D View this record in MEDLINE/PubMed |
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| Keywords | 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 |
| Language | English |
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| SubjectTerms | 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 |
| Title | Learning Graph Matching |
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