An eigenspace projection clustering method for inexact graph matching

Author

Caelli, Terry ; Kosinov, Serhiy

Author_Institution

Dept. of Comput. Sci., Alberta Univ., Edmonton, Alta., Canada

Volume

26

Issue

4

fYear

2004

fDate

4/1/2004 12:00:00 AM

Firstpage

515

Lastpage

519

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.

Keywords

computer vision; eigenvalues and eigenfunctions; graph theory; image matching; pattern clustering; eigenspace projection clustering; eigenspace renormalization projection clustering; inexact graph matching; random graphs; relational clustering; shock graph based shape matching; Character recognition; Clustering methods; Computer vision; Eigenvalues and eigenfunctions; Electric shock; Matrix decomposition; Object recognition; Pattern matching; Shape; Symmetric matrices; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Graphics; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2004.1265866

Filename

1265866