Anytime graph matching

被引:9
作者
Abu-Aisheh, Zeina [1 ]
Raveaux, Romain [1 ]
Ramel, Jean-Yves [1 ]
机构
[1] Univ Francois Rabelais Tours, Lab Informat, 64 Ave Jean Portalis, F-37200 Tours, France
关键词
Graph matching; Anytime algorithms; Pattern recognition; Graph edit distance; Performance evaluation metrics; ALGORITHMS; ASSIGNMENT;
D O I
10.1016/j.patrec.2016.10.004
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 propose and explain the use of anytime algorithms in graph matching (GM). GM methods have been involved in many pattern recognition problems. In such a context, GM methods are part of a more complex retrieval system that imposes time and memory constraints on such methods. Anytime algorithms are well suited for use in such an uncertain environment. An anytime algorithm quickly provides the first solution to the problem, finds a list of improved solutions and eventually converges to the optimal solution instead of providing one and only one solution (i.e., the optimal solution). We describe how to convert a recent depth-first GM method into an anytime one. By constraining the solver, the algorithm creates an anytime heuristic search algorithm that allows a flexible trade-off between the search time and the solution quality. We analyze the properties of the resulting anytime algorithm and consider its performance in terms of the deviation of the provided solution from the optimal or the best one found by a state-of-the-art method. Experiments were carried out on seven different types of graph datasets. Moreover, the adopted algorithm was compared to four approximate error-tolerant GM methods. Results showed that the anytime GM can outperform suboptimal methods by only waiting for a small amount of supplementary time. This conclusion brings into question the usual evidence that claims that it is impossible to use optimal GM methods in real-world applications. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:215 / 224
页数:10
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