Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107290
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Type: Conference paper
Title: Pairwise matching through max-weight bipartite belief propagation
Author: Zhang, Z.
Shi, Q.
McAuley, J.
Wei, W.
Zhang, Y.
Van Den Hengel, A.
Citation: Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016), 2016 / vol.2016, pp.1202-1210
Publisher: IEEE
Issue Date: 2016
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781467388511
ISSN: 1063-6919
Conference Name: 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) (26 Jun 2016 - 01 Jul 2016 : Las Vegas, NV)
Statement of
Responsibility: 
Zhen Zhang, Qinfeng Shi, Julian McAuley, Wei Wei, Yanning Zhang, Anton van den Hengel
Abstract: Feature matching is a key problem in computer vision and pattern recognition. One way to encode the essential interdependence between potential feature matches is to cast the problem as inference in a graphical model, though recently alternatives such as spectral methods, or approaches based on the convex-concave procedure have achieved the state-of-the-art. Here we revisit the use of graphical models for feature matching, and propose a belief propagation scheme which exhibits the following advantages: (1) we explicitly enforce one-to-one matching constraints, (2) we offer a tighter relaxation of the original cost function than previous graphical-model-based approaches, and (3) our sub-problems decompose into max-weight bipartite matching, which can be solved efficiently, leading to orders-of-magnitude reductions in execution time. Experimental results show that the proposed algorithm produces results superior to those of the current state-of-the-art.
Rights: © 2016 IEEE
RMID: 0030056379
DOI: 10.1109/CVPR.2016.135
Grant ID: http://purl.org/au-research/grants/arc/DP140102270
http://purl.org/au-research/grants/arc/DP160100703
Appears in Collections:Computer Science publications

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