Fast linear sum assignment with error-correction and no cost constraints

Sébastien Bougleux (1) , Benoit Gaüzère (2, 3) , David Blumenthal (4) , Luc Brun (1)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
3 DocApp - LITIS - Equipe Apprentissage
4 Free University of Bozen-Bolzano

We propose an algorithm that efficiently solves the linear sum assignment problem with error-correction and no cost constraints. This problem is encountered for instance in the approximation of the graph edit distance. The fastest currently available solvers for the linear sum assignment problem require the pairwise costs to respect the triangle inequality. Our algorithm is as fast as these algorithms, but manages to drop the cost constraint. The main technical ingredient of our algorithm is a cost-dependent factorization of the node substitutions.

Domains: Computer Science [cs]; Machine Learning [cs.LG]

Sébastien Bougleux, Benoit Gaüzère, David Blumenthal, Luc Brun. Fast linear sum assignment with error-correction and no cost constraints. Pattern Recognition Letters, 2018, ⟨10.1016/j.patrec.2018.03.032⟩. ⟨hal-02110718⟩