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Generalised median of graph correspondences.

Moreno-García, Carlos Francisco; Serratosa, Francesc

Authors

Francesc Serratosa



Abstract

A graph correspondence is defined as a function that maps the elements of two attributed graphs. Due to the increasing availability of methods to perform graph matching, numerous graph correspondences can be deducted for a pair of attributed graphs. To obtain a representative prototype for a set of data structures, the concept of the median has been largely employed, as it has proven to deliver a robust sample. Nonetheless, the calculation of the exact (or generalised) median is known to be an NP-complete problem for most domains. In this paper, we present a method based on an optimisation function to calculate the generalised median graph correspondence. This method makes use of the Correspondence Edit Distance, which is a metric that considers the attributes and the local structures of the graphs to obtain more interesting and meaningful results. Experimental validation shows that this approach is capable of obtaining the generalised median in a comparable runtime with respect to state-of-the-art methods on artificial data, while maintaining the success rate for a real-application case.

Citation

MORENO-GARCÍA, C.F. and SERRATOSA, F. 2019. Generalised median of graph correspondences. Pattern recognition letters [online], 125, pages 389-395. Available from: https://doi.org/10.1016/j.patrec.2019.05.015

Journal Article Type Article
Acceptance Date May 19, 2019
Online Publication Date May 21, 2019
Publication Date Jul 1, 2019
Deposit Date Jun 17, 2019
Publicly Available Date May 22, 2020
Journal Pattern Recognition Letters
Print ISSN 0167-8655
Electronic ISSN 1872-7344
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 125
Pages 389-395
DOI https://doi.org/10.1016/j.patrec.2019.05.015
Keywords Signal processing; Software; Artificial intelligence; Computer vision and pattern recognition
Public URL https://rgu-repository.worktribe.com/output/310334
Contract Date Jun 17, 2019

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