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  Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems

Bernard, F., Theobalt, C., & Moeller, M. (2017). Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems. Retrieved from http://arxiv.org/abs/1711.10733.

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arXiv:1711.10733.pdf (Preprint), 3MB
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 Creators:
Bernard, Florian1, Author           
Theobalt, Christian1, Author           
Moeller, Michael2, Author
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
2External Organizations, ou_persistent22              

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Free keywords: Mathematics, Optimization and Control, math.OC,Computer Science, Computer Vision and Pattern Recognition, cs.CV,Statistics, Machine Learning, stat.ML
 Abstract: In this work we study convex relaxations of quadratic optimisation problems over permutation matrices. While existing semidefinite programming approaches can achieve remarkably tight relaxations, they have the strong disadvantage that they lift the original $n {\times} n$-dimensional variable to an $n^2 {\times} n^2$-dimensional variable, which limits their practical applicability. In contrast, here we present a lifting-free convex relaxation that is provably at least as tight as existing (lifting-free) convex relaxations. We demonstrate experimentally that our approach is superior to existing convex and non-convex methods for various problems, including image arrangement and multi-graph matching.

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Language(s): eng - English
 Dates: 2017-11-292017
 Publication Status: Published online
 Pages: 12 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1711.10733
URI: http://arxiv.org/abs/1711.10733
BibTex Citekey: Bernard2017
 Degree: -

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