DocumentCode :
639422
Title :
A Fast Semidefinite Approach to Solving Binary Quadratic Problems
Author :
Peng Wang ; Chunhua Shen ; van den Hengel, A.
Author_Institution :
Sch. of Comput. Sci., Univ. of Adelaide, Adelaide, SA, Australia
fYear :
2013
fDate :
23-28 June 2013
Firstpage :
1312
Lastpage :
1319
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.
Keywords :
computational complexity; computer vision; image registration; image segmentation; quadratic programming; binary quadratic problem solving; clustering method; computational complexity; computer vision problems; dual optimization approach; fast semidefinite approach; image registration; image segmentation; semidefinite programming; spectral methods; spectral relaxation; Complexity theory; Computer vision; Image segmentation; Linear programming; Optimization; Symmetric matrices; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location :
Portland, OR
ISSN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2013.173
Filename :
6619017
Link To Document :
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