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Anytime graph matching

Zeina Abu-Aisheh 1 Romain Raveaux 1 Jean-Yves Ramel 1
1 RFAI - Reconnaissance des formes et analyse d'images
LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
Abstract : 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 an error-tolerant GM method into an anytime one. A depth-first GM method has been recently proposed in the literature. This algorithm requires less memory and improves the upper bound while exploring the search tree. It finds the first suboptimal solution quickly, and then keeps on searching for a list of improved solutions. The algorithm is well suited for conversion into an anytime algorithm. By constraining the solver, it 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 just 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.
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Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel. Anytime graph matching. Pattern Recognition Letters, Elsevier, 2016, 84, pp.215--224. ⟨10.1016/j.patrec.2016.10.004⟩. ⟨hal-01490832⟩

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