Graph edit distance contest: Results and future challenges

Zeina Abu-Aisheh (1) , Benoit Gaüzère (2) , Sébastien Bougleux (3) , Jean-Yves Ramel (1) , Luc Brun (3) , Romain Raveaux (1) , Pierre Héroux (4, 2) , Sébastien Adam (4, 2)

1 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
2 DocApp - LITIS - Equipe Apprentissage
3 Equipe Image - Laboratoire GREYC - UMR6072
4 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

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).

Domains: Image Processing [eess.IV]; Computer Vision and Pattern Recognition [cs.CV]

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, 2017, 100, pp.96-103. ⟨10.1016/j.patrec.2017.10.007⟩. ⟨hal-01624592⟩