An eigenspace projection clustering method for inexact graph matching

被引：132

作者：

Caelli, T ^{[1
]}

Kosinov, S ^{[1
]}

机构：

[1] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2H1, Canada

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2004年 / 26卷 / 04期

关键词：

inexact multisubgraph matching; eigendecom position; eigenspace projections; correspondence clustering; shape matching; random graphs;

D O I：

10.1109/TPAMI.2004.1265866

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：515 / 519

页数：5

共 15 条

[1] CLUSTER SEPARATION MEASURE [J].