Dynamic programming bipartite belief propagation for hyper graph matching
Date
2017
Authors
Zhang, Z.
McAuley, J.
Li, Y.
Wei, W.
Zhang, Y.
Shi, Q.
Editors
Sierra, C.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
IJCAI : proceedings of the conference / sponsored by the International Joint Conferences on Artificial Intelligence, 2017 / Sierra, C. (ed./s), vol.0, pp.4662-4668
Statement of Responsibility
Zhen Zhang, Julian McAuley, Yong Li, Wei Wei, Yanning Zhang, Qinfeng Shi
Conference Name
International Joint Conference on Artificial Intelligence (IJCAI 2017) (19 Aug 2017 - 25 Aug 2017 : Melbourne, Australia)
Abstract
Hyper graph matching problems have drawn attention recently due to their ability to embed higher order relations between nodes. In this paper, we formulate hyper graph matching problems as constrained MAP inference problems in graphical models. Whereas previous discrete approaches introduce several global correspondence vectors, we introduce only one global correspondence vector, but several local correspondence vectors. This allows us to decompose the problem into a (linear) bipartite matching problem and several belief propagation sub-problems. Bipartite matching can be solved by traditional approaches, while the belief propagation sub-problem is further decomposed as two sub-problems with optimal substructure. Then a newly proposed dynamic programming procedure is used to solve the belief propagation sub-problem. Experiments show that the proposed methods outperform state-of-the-art techniques for hyper graph matching.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
copyright status unknown