Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Pairwise matching through max-weight bipartite belief propagation|
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|
|Series/Report no.:||IEEE Conference on Computer Vision and Pattern Recognition|
|Conference Name:||29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) (26 Jun 2016 - 1 Jul 2016 : Las Vegas, NV)|
|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|
|Appears in Collections:||Aurora harvest 3|
Computer Science publications
Files in This Item:
|Restricted Access||605.33 kB||Adobe PDF||View/Open|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.