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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2017.2730884
|2 doi
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|a eng
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| 100 |
1 |
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|a Shekhovtsov, Alexander
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Maximum Persistency via Iterative Relaxed Inference in Graphical Models
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| 264 |
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|c 2018
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|a Text
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|a ƒaComputermedien
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|a Date Completed 05.04.2019
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|a Date Revised 05.04.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision
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| 650 |
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|a Journal Article
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Swoboda, Paul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Savchynskyy, Bogdan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shekhovtsov, Alexander
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Swoboda, Paul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Savchynskyy, Bogdan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Savchynskyy, Bogdan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shekhovtsov, Alexander
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|4 aut
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| 700 |
1 |
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|a Swoboda, Paul
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 40(2018), 7 vom: 25. Juli, Seite 1668-1682
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:40
|g year:2018
|g number:7
|g day:25
|g month:07
|g pages:1668-1682
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|u http://dx.doi.org/10.1109/TPAMI.2017.2730884
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