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An eigenspace projection clustering method for inexact graph matching.
IEEE Trans Pattern Anal Mach Intell. 2004 Apr; 26(4):515-9.IT

Abstract

In this paper, we show how inexact graph matching (that is, the correspondence between sets of vertices of pairs of graphs) can be solved using the renormalization of projections of the vertices (as defined in this case by their connectivities) into the joint eigenspace of a pair of graphs and a form of relational clustering. An important feature of this eigenspace renormalization projection clustering (EPC) method is its ability to match graphs with different number of vertices. Shock graph-based shape matching is used to illustrate the model and a more objective method for evaluating the approach using random graphs is explored with encouraging results.

Authors+Show Affiliations

Caelli T
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada, T6G 2H1. tcaelli@ualberta.ca
Kosinov S
No affiliation info available

MeSH

AlgorithmsArtificial IntelligenceCluster AnalysisComputer GraphicsComputer SimulationImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedSubtraction Technique

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Validation Study

Language

eng

PubMed ID

15382655

Citation

Caelli, Terry, and Serhiy Kosinov. "An Eigenspace Projection Clustering Method for Inexact Graph Matching." IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 26, no. 4, 2004, pp. 515-9.
Caelli T, Kosinov S. An eigenspace projection clustering method for inexact graph matching. IEEE Trans Pattern Anal Mach Intell. 2004;26(4):515-9.
Caelli, T., & Kosinov, S. (2004). An eigenspace projection clustering method for inexact graph matching. IEEE Transactions On Pattern Analysis and Machine Intelligence, 26(4), 515-9.
Caelli T, Kosinov S. An Eigenspace Projection Clustering Method for Inexact Graph Matching. IEEE Trans Pattern Anal Mach Intell. 2004;26(4):515-9. PubMed PMID: 15382655.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An eigenspace projection clustering method for inexact graph matching. AU - Caelli,Terry, AU - Kosinov,Serhiy, PY - 2004/9/24/pubmed PY - 2004/10/20/medline PY - 2004/9/24/entrez SP - 515 EP - 9 JF - IEEE transactions on pattern analysis and machine intelligence JO - IEEE Trans Pattern Anal Mach Intell VL - 26 IS - 4 N2 - In this paper, we show how inexact graph matching (that is, the correspondence between sets of vertices of pairs of graphs) can be solved using the renormalization of projections of the vertices (as defined in this case by their connectivities) into the joint eigenspace of a pair of graphs and a form of relational clustering. An important feature of this eigenspace renormalization projection clustering (EPC) method is its ability to match graphs with different number of vertices. Shock graph-based shape matching is used to illustrate the model and a more objective method for evaluating the approach using random graphs is explored with encouraging results. SN - 0162-8828 UR - https://www.unboundmedicine.com/medline/citation/15382655/An_eigenspace_projection_clustering_method_for_inexact_graph_matching_ DB - PRIME DP - Unbound Medicine ER -