Title :
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
fDate :
4/1/2004 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2004.1265866
