A fast projected fixed-point algorithm for large graph matching

We propose a fast algorithm for approximate matching of large graphs. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability. By using a new doubly stochastic projection, for matching two weighted graphs of n nodes, our algorithm has t...

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Bibliographic Details
Published in:Pattern recognition Vol. 60; pp. 971 - 982
Main Authors: Lu, Yao, Huang, Kaizhu, Liu, Cheng-Lin
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2016
Subjects:
Feature correspondence
Graph matching
Large graph algorithm
Point matching
Projected fixed-point
Projected fixed-point
Feature correspondence
Large graph algorithm
Point matching
Graph matching
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary:We propose a fast algorithm for approximate matching of large graphs. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability. By using a new doubly stochastic projection, for matching two weighted graphs of n nodes, our algorithm has time complexity only O(n3) per iteration and space complexity O(n2). We proved that our algorithm converges at a super-logarithmic rate. Experiments on large synthetic and real graphs (over 1000 nodes) were conducted to evaluate the performance of various algorithms. Results show that due to its fast convergence, our algorithm is more than an order of magnitude faster than the previous state-of-the-art algorithms, while maintaining comparable accuracy in large graph matching. •Low time complexity O(n3)/iteration for two graphs of n nodes.•Super-logarithm convergence guarantee.•Large graph matching experiments.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2016.07.015