A convex relaxation for multi-graph matching

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Item Type:	Conference or Workshop Item
Title:	A convex relaxation for multi-graph matching
Creators Name:	Swoboda, P., Kainmüller, D., Mokarian, A., Theobalt, C. and Bernard, F.
Abstract:	We present a convex relaxation for the multi-graph matching problem. Our formulation allows for partial pairwise matchings, guarantees cycle consistency, and our objective incorporates both linear and quadratic costs. Moreover, we also present an extension to higher-order costs. In order to solve the convex relaxation we employ a message passing algorithm that optimizes the dual problem. We experimentally compare our algorithm on established benchmark problems from computer vision, as well as on large problems from biological image analysis, the size of which exceed previously investigated multi-graph matching instances.
Keywords:	Optimization Methods, Segmentation, Grouping and Shape
Source:	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Title of Book:	2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISSN:	1063-6919
Publisher:	IEEE
Page Range:	11148-11157
Date:	2019
Additional Information:	Copyright © 2019 IEEE. These CVPR 2019 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.
Official Publication:	https://doi.org/10.1109/CVPR.2019.01141
External Fulltext:	View full text on external repository or document server

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