Graph edit distance contest: Results and future challenges

Zeina Abu-Aisheh 1 Benoit Gaüzère 2 Sébastien Bougleux 2 Jean-Yves Ramel 1 Luc Brun 2 Romain Raveaux 1 Pierre Héroux 3 Sébastien Adam 3
1 LI - Laboratoire d'Informatique de l'Université de Tours
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
3 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : Graph Distance Contest (GDC) was organized in the context of ICPR 2016. Its main challenge was to inspect and report performances and effectiveness of exact and approximate graph edit distance methods by comparison with a ground truth. This paper presents the context of this competition, the metrics and datasets used for evaluation, and the results obtained by the eight submitted methods. Results are analyzed and discussed in terms of computation time and accuracy. We also highlight the future challenges in graph edit distance regarding both future methods and evaluation metrics. The contest was supported by the Technical Committee on Graph-Based Representations in Pattern Recognition (TC-15) of the International Association of Pattern Recognition (IAPR).
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Article dans une revue
Pattern Recognition Letters, Elsevier, 2017, 100, pp.96-103. 〈10.1016/j.patrec.2017.10.007〉
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Contributeur : Romain Raveaux <>
Soumis le : jeudi 26 octobre 2017 - 15:14:12
Dernière modification le : mardi 24 avril 2018 - 01:38:50

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Zeina Abu-Aisheh, Benoit Gaüzère, Sébastien Bougleux, Jean-Yves Ramel, Luc Brun, et al.. Graph edit distance contest: Results and future challenges. Pattern Recognition Letters, Elsevier, 2017, 100, pp.96-103. 〈10.1016/j.patrec.2017.10.007〉. 〈hal-01624592〉

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