Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/92324
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Type: Journal article
Title: An adversarial optimization approach to efficient outlier removal
Author: Yu, J.
Eriksson, A.
Chin, T.
Suter, D.
Citation: Journal of Mathematical Imaging and Vision, 2014; 48(3):451-466
Publisher: Kluwer Academic Publishers
Issue Date: 2014
ISSN: 0924-9907
1573-7683
Statement of
Responsibility: 
Jin Yu, Anders Eriksson, Tat-Jun Chin, David Suter
Abstract: This paper proposes a novel adversarial optimiza- tion approach to efficient outlier removal in computer vision. We characterize the outlier removal problem as a game that involves two players of conflicting interests, namely, model optimizer and outliers. Such an adversarial view not only brings new insights into some existing methods, but also gives rise to a general optimization framework that provably unifies them. Under the proposed framework, we develop a new outlier removal approach that is able to offer a much needed control over the trade-off between reliability and speed, which is usually not available in previous methods. Underlying the proposed approach is a mixed-integer minmax (convex-concave) problem formulation. Although a minmax problem is generally not amenable to efficient optimization, we show that for some commonly used vision objective functions, an equivalent Linear Program reformulation exists. This significantly simplifies the optimization. We demonstrate our method on two representative multiview geometry problems. Experiments on real image data illustrate superior practical performance of our method over recent techniques.
Keywords: Mixed-integer optimization; convex relaxation; model fitting; outlier removal
Rights: © Springer Science+Business Media New York 2013
RMID: 0030007543
DOI: 10.1007/s10851-013-0418-7
Grant ID: http://purl.org/au-research/grants/arc/DP0878801
http://purl.org/au-research/grants/arc/DP0988439
Appears in Collections:Computer Science publications

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