Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107290
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dc.contributor.author | Zhang, Z. | - |
dc.contributor.author | Shi, Q. | - |
dc.contributor.author | McAuley, J. | - |
dc.contributor.author | Wei, W. | - |
dc.contributor.author | Zhang, Y. | - |
dc.contributor.author | Van Den Hengel, A. | - |
dc.date.issued | 2016 | - |
dc.identifier.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 | - |
dc.identifier.isbn | 9781467388511 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/2440/107290 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Zhen Zhang, Qinfeng Shi, Julian McAuley, Wei Wei, Yanning Zhang, Anton van den Hengel | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE Conference on Computer Vision and Pattern Recognition | - |
dc.rights | © 2016 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/cvpr.2016.135 | - |
dc.title | Pairwise matching through max-weight bipartite belief propagation | - |
dc.type | Conference paper | - |
dc.contributor.conference | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) (26 Jun 2016 - 1 Jul 2016 : Las Vegas, NV) | - |
dc.identifier.doi | 10.1109/CVPR.2016.135 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP140102270 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP160100703 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Zhang, Z. [0000-0003-2805-4396] | - |
dc.identifier.orcid | Shi, Q. [0000-0002-9126-2107] | - |
dc.identifier.orcid | Van Den Hengel, A. [0000-0003-3027-8364] | - |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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RA_hdl_107290.pdf Restricted Access | Restricted Access | 605.33 kB | Adobe PDF | View/Open |
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