A graph repository for learning error-tolerant graph matching.

Moreno-Garc�a, Carlos Francisco; Cort�s, Xavier; Serratosa, Francesc

Abstract

In the last years, efforts in the pattern recognition field have been especially focused on developing systems that use graph based representations. To that aim, some graph repositories have been presented to test graph-matching algorithms or to learn some parameters needed on such algorithms. The aim of these tests has always been to increase the recognition ratio in a classification framework. Nevertheless, some graph-matching applications are not solely intended for classification purposes, but to detect similarities between the local parts of the objects that they represent. Thus, current state of the art repositories provide insufficient information. We present a graph repository structure such that each register is not only composed of a graph and its class, but also of a pair of graphs and a ground-truth correspondence between them, as well as their class. This repository structure is useful to analyse and develop graph-matching algorithms and to learn their parameters in a broadly manner. We present seven different databases, which are publicly available, with these structure and present some quality measures experimented on them.

Citation

MORENO-GARCÍA, C.F., CORTÉS, X. and SERRATOSA, F. 2016. A graph repository for learning error-tolerant graph matching. In Robles-Kelly, A., Loog, M., Biggio, B., Escolano, F. and Wilson, R. (eds.) Structural, syntactic and statistical pattern recognition: proceedings of 2016 Joint International Association of Pattern Recognition (IAPR) Structural and syntactic pattern recognition internaional workshops (SSPR 2016), and Statistical techniques in pattern recognition (SPR 2016) (S+SSPR 2016), 20 November - 2 December 2016, Mérida, Mexico. Lecture notes in computer science, 10029. Cham: Springer [online], pages 519-529. Available from: https://doi.org/10.1007/978-3-319-49055-7_46

• Conference Name: 2016 Joint International Association of Pattern Recognition (IAPR) Structural and syntactic pattern recognition internaional workshops (SSPR 2016), and Statistical techniques in pattern recognition (SPR 2016) (S+SSPR 2016)
• Conference Location: Mérida, Mexico
• Start Date: Nov 30, 2016
• End Date: Dec 2, 2016
• Acceptance Date: Sep 5, 2016
• Online Publication Date: Nov 5, 2016
• Publication Date: Dec 31, 2016
• Deposit Date: Oct 14, 2020
• Publicly Available Date: Jan 11, 2021
• Publisher: Springer
• Volume: 10029
• Pages: 519-529
• Series Title: Lecture notes in computer science
• Series ISSN: 0302-9743
• Book Title: Structural, syntactic and statistical pattern recognition: proceedings of 2016 Joint International Association of Pattern Recognition (IAPR) Structural and syntactic pattern recognition internaional workshops (SSPR 2016), and Statistical techniques in pat
• ISBN: 9783319490540
• DOI: https://doi.org/10.1007/978-3-319-49055-7_46
• Keywords: Graph database; Graph-matching algorithm; Graph-learning algorithm
• Public URL: https://rgu-repository.worktribe.com/output/976248