NDLI: A Fast Semidefinite Approach to Solving Binary Quadratic Problems

Content Provider	IEEE Xplore Digital Library
Author	Peng Wang Chunhua Shen van den Hengel, A.
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci., Univ. of Adelaide, Adelaide, SA, Australia (Peng Wang; Chunhua Shen; van den Hengel, A.)
Abstract	Many computer vision problems can be formulated as binary quadratic programs (BQPs). Two classic relaxation methods are widely used for solving BQPs, namely, spectral methods and semi definite programming (SDP), each with their own advantages and disadvantages. Spectral relaxation is simple and easy to implement, but its bound is loose. Semi definite relaxation has a tighter bound, but its computational complexity is high for large scale problems. We present a new SDP formulation for BQPs, with two desirable properties. First, it has a similar relaxation bound to conventional SDP formulations. Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods. Extensive experiments on various applications including clustering, image segmentation, co-segmentation and registration demonstrate the usefulness of our SDP formulation for solving large-scale BQPs.
Starting Page	1312
Ending Page	1319
File Size	1656039
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.173
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Computer vision Optimization Complexity theory Image segmentation Linear programming Symmetric matrices
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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