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  • We address the problem of energy minimization, which is (1) generally NP-complete and (2) involves many discrete variables - commonly a 2D array of them, arising from an MRF model. One of the approaches to the problem is to formulate it as integer linear programming and relax integrality constraints. However this can be done in a number of possible ways. One, widely applied previously (LP-1) [19, 13, 4, 22, 9, 23], appears to lead to a large-scale linear program which is not practical to solve with general LP methods. A number of algorithms were developed which attempt to solve the problem exploiting its structure [14, 23, 22, 9], however their common drawback is that they may converge to a suboptimal point. The other LP relaxation we consider here is constructed by (1) refor- mulating the optimization problem in the form of a function of binary vari- ables [18], and (2) applying the roof duality relaxation [6] to the reformulated problem. We refer to the resulting relaxation as LP-2.
  • We address the problem of energy minimization, which is (1) generally NP-complete and (2) involves many discrete variables - commonly a 2D array of them, arising from an MRF model. One of the approaches to the problem is to formulate it as integer linear programming and relax integrality constraints. However this can be done in a number of possible ways. One, widely applied previously (LP-1) [19, 13, 4, 22, 9, 23], appears to lead to a large-scale linear program which is not practical to solve with general LP methods. A number of algorithms were developed which attempt to solve the problem exploiting its structure [14, 23, 22, 9], however their common drawback is that they may converge to a suboptimal point. The other LP relaxation we consider here is constructed by (1) refor- mulating the optimization problem in the form of a function of binary vari- ables [18], and (2) applying the roof duality relaxation [6] to the reformulated problem. We refer to the resulting relaxation as LP-2. (en)
  • We address the problem of energy minimization, which is (1) generally NP-complete and (2) involves many discrete variables - commonly a 2D array of them, arising from an MRF model. One of the approaches to the problem is to formulate it as integer linear programming and relax integrality constraints. However this can be done in a number of possible ways. One, widely applied previously (LP-1) [19, 13, 4, 22, 9, 23], appears to lead to a large-scale linear program which is not practical to solve with general LP methods. A number of algorithms were developed which attempt to solve the problem exploiting its structure [14, 23, 22, 9], however their common drawback is that they may converge to a suboptimal point. The other LP relaxation we consider here is constructed by (1) refor- mulating the optimization problem in the form of a function of binary vari- ables [18], and (2) applying the roof duality relaxation [6] to the reformulated problem. We refer to the resulting relaxation as LP-2. (cs)
Title
  • LP-relaxation of binarized energy minimization
  • LP-relaxation of binarized energy minimization (en)
  • LP-relaxation of binarized energy minimization (cs)
skos:prefLabel
  • LP-relaxation of binarized energy minimization
  • LP-relaxation of binarized energy minimization (en)
  • LP-relaxation of binarized energy minimization (cs)
skos:notation
  • RIV/68407700:21230/08:03150825!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
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  • P(7E08031), Z(MSM6840770038)
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  • 377259
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  • RIV/68407700:21230/08:03150825
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  • MRF; energy minimization; multilabe; partial optimality; persistency; pseudo-Boolean optimization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D5CE2BB68630]
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http://linked.open...iv/tvurceVysledku
  • Hlaváč, Václav
  • Shekhovtsov, Oleksandr
  • Kohli, P.
  • Rother, C.
  • Kolmogorov, V.
  • Torr, P.
http://linked.open...n/vavai/riv/zamer
http://localhost/t...ganizacniJednotka
  • 21230
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