Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
نویسندگان
چکیده
منابع مشابه
Efficient and Exact MAP-MRF Inference using Branch and Bound
We propose two novel Branch-and-Bound (BB) methods to efficiently solve exact MAPMRF inference on problems with a large number of states (per variable) H. By organizing the data in a suitable structure, the time complexity of our best method for evaluating the bound at each branch is reduced from O(H) to O(H). This permits searching for the MAP solution in O(BH +Q) instead of O(BH) (without usi...
متن کاملSupplementary Material: Efficient and Exact MAP-MRF Inference using Branch and Bound
By default, the edge-consistent LPR is solved using Message Passing (MP) algorithm to initialize β until convergence or for at most 1000 iterations, whichever comes first. If the gap between the upper and lower bounds is not smaller than 10 (stopping criteria) already, we further apply our BB method or MPLP-CP method [4]. Both methods stop when the same stopping criteria (gap < 10) is reached. ...
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Branch-and-cut methods are exact algorithms for integer programming problems. They consist of a combination of a cutting plane method with a branch-and-bound algorithm. These methods work by solving a sequence of linear programming relaxations of the integer programming problem. Cutting plane methods improve the relaxation of the problem to more closely approximate the integer programming probl...
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We propose a new segmentation or clustering model that combines Markov Random Field (MRF) and Normalized Cut (NC) objectives. Both NC and MRF models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show th...
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ژورنال
عنوان ژورنال: International Journal of Computer Vision
سال: 2015
ISSN: 0920-5691,1573-1405
DOI: 10.1007/s11263-015-0865-2