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Jens Keuchel, Christoph Schn?, Christian Schellewald, Daniel Cremers, "Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, pp. 13641379, November, 2003.  
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@article{ 10.1109/TPAMI.2003.1240111, author = {Jens Keuchel and Christoph Schn? and Christian Schellewald and Daniel Cremers}, title = {Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {25}, number = {11}, issn = {01628828}, year = {2003}, pages = {13641379}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2003.1240111}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming IS  11 SN  01628828 SP1364 EP1379 EPD  13641379 A1  Jens Keuchel, A1  Christoph Schn?, A1  Christian Schellewald, A1  Daniel Cremers, PY  2003 KW  Image partitioning KW  segmentation KW  graph cuts KW  perceptual grouping KW  figureground discrimination KW  combinatorial optimization KW  relaxation KW  convex optimization KW  convex programming. VL  25 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—We introduce a novel optimization method based on semidefinite programming relaxations to the field of computer vision and apply it to the combinatorial problem of minimizing quadratic functionals in binary decision variables subject to linear constraints. The approach is (tuning) parameterfree and computes highquality combinatorial solutions using interiorpoint methods (convex programming) and a randomized hyperplane technique. Apart from a symmetry condition, no assumptions (such as metric pairwise interactions) are made with respect to the objective criterion. As a consequence, the approach can be applied to a wide range of problems. Applications to unsupervised partitioning, figureground discrimination, and binary restoration are presented along with extensive groundtruth experiments. From the viewpoint of relaxation of the underlying combinatorial problem, we show the superiority of our approach to relaxations based on spectral graph theory and prove performance bounds.
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