A weight regularized relaxation based graph matching algorithm

Authors:
Zhi-Yong Liu;Hong Qiao;Lei Xu
Affiliations:
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China;State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, Hong Kong
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

Citing 8
Cited 0

An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Eigendecomposition Approach to Weighted Graph Matching Problems

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graduated Assignment Algorithm for Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Simulated Annealing, Boltzmann Machine, and Attributed Graph Matching

Proceedings of the EURASIP Workshop 1990 on Neural Networks
Linear time algorithm for isomorphism of planar graphs (Preliminary Report)

STOC '74 Proceedings of the sixth annual ACM symposium on Theory of computing
Convex Optimization

Convex Optimization
A Path Following Algorithm for the Graph Matching Problem

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Extended Path Following Algorithm for Graph-Matching Problem

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a regularized relaxation based graph matching algorithm. The graph matching problem is formulated as a constrained convex quadratic program, by relaxing the permutation matrix to a doubly stochastic one. To gradually push the doubly stochastic matrix back to a permutation one, a simple weighted concave regular term is added to the convex objective function. The concave regular function is not a concave relaxation of the original matching problem. However, it is shown that such a simple concave regular term has a comparative performance as the concave relaxation of the PATH following algorithm, which works only on undirected graphs. A concave-convex procedure (CCCP) together with the Frank-Wolfe algorithm is adopted to solve the matching problem, and some experimental results witness the state-of-art performance of the proposed algorithm.