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https://hdl.handle.net/2440/134877
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Type: | Conference paper |
Title: | Dynamic programming bipartite belief propagation for hyper graph matching |
Author: | Zhang, Z. McAuley, J. Li, Y. Wei, W. Zhang, Y. Shi, Q. |
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 |
Publisher: | AAAI Press |
Publisher Place: | online |
Issue Date: | 2017 |
ISBN: | 9780999241103 |
ISSN: | 1045-0823 |
Conference Name: | International Joint Conference on Artificial Intelligence (IJCAI 2017) (19 Aug 2017 - 25 Aug 2017 : Melbourne, Australia) |
Editor: | Sierra, C. |
Statement of Responsibility: | Zhen Zhang, Julian McAuley, Yong Li, Wei Wei, Yanning Zhang, Qinfeng Shi |
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. |
Keywords: | Uncertainty in AI: Approximate Probabilistic Inference; Uncertainty in AI: Graphical Models; Robotics and Vision: Localization; Mapping; State Estimation |
Rights: | copyright status unknown |
DOI: | 10.24963/ijcai.2017/650 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102270 http://purl.org/au-research/grants/arc/DP160100703 |
Published version: | https://www.aaai.org/Press/press.php |
Appears in Collections: | Computer Science publications |
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