Please use this identifier to cite or link to this item:
https://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 / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 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 - 1 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 |
DOI: | 10.1109/CVPR.2016.135 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102270 http://purl.org/au-research/grants/arc/DP160100703 |
Published version: | http://dx.doi.org/10.1109/cvpr.2016.135 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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