Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?

Autor:	Xu, Zhoubo, Chen, Puqing, Raveaux, Romain, Yang, Xin, Liu, Huadong
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
DOI:	10.1016/j.patcog.2024.110697
Popis:	Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate graph matching with deep learning. However, there is no research to explain what role the graph matching algorithm plays in the model. Therefore, we propose an approach integrating a MILP formulation of the graph matching problem. This formulation is solved to optimal and it provides inherent baseline. Meanwhile, similar approaches are derived by releasing the optimal guarantee of the graph matching solver and by introducing a quality level. This quality level controls the quality of the solutions provided by the graph matching solver. In addition, several relaxations of the graph matching problem are put to the test. Our experimental evaluation gives several theoretical insights and guides the direction of deep graph matching methods. Comment: The paper is under consideration at Pattern Recognition
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.00394 Zobrazit plný text záznamu View this record from Arxiv