IEEE Trans Pattern Anal Mach Intell - What Is Optimized in Convex Relaxations for Multi-Label Problems: Connecting Discrete and Continuously-Inspired MAP Inference.

Tópicos

{ problem(2511) optim(1539) algorithm(950) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(984) reconstruct(947) comput(926) }
{ imag(2830) propos(1344) filter(1198) }
{ monitor(1329) mobil(1314) devic(1160) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ bind(1733) structur(1185) ligand(1036) }
{ visual(1396) interact(850) tool(830) }
{ first(2504) two(1366) second(1323) }
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{ high(1669) rate(1365) level(1280) }
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{ method(1557) propos(1049) approach(1037) }
{ featur(1941) imag(1645) propos(1176) }
{ research(1085) discuss(1038) issu(1018) }
{ model(3480) simul(1196) paramet(876) }
{ data(3008) multipl(1320) sourc(1022) }
{ use(976) code(926) identifi(902) }
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{ take(945) account(800) differ(722) }
{ error(1145) method(1030) estim(1020) }
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{ imag(2675) segment(2577) method(1081) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this work we present a unified view on Markov random fields and recently proposed continuous tight convex relaxations for multi-label assignment in the image plane. These relaxations are far less biased towards the grid geometry than Markov random fields (MRFs) on grids. It turns out that the continuous methods are non-linear extensions of the well-established local polytope MRF relaxation. In view of this result a better understanding of these tight convex relaxations in the discrete setting is obtained. Further, a wider range of optimization methods is now applicable to find a minimizer of the tight formulation. We propose two methods to improve the efficiency of minimization. One uses a weaker, but more efficient continuously inspired approach as initialization and gradually refines the energy where it is necessary. The other one reformulates the dual energy enabling smooth approximations to be used for efficient optimization. We demonstrate the utility of our proposed minimization schemes in numerical experiments. Finally, we generalize the underlying energy formulation from isotropic metric smoothness costs to arbitrary non-metric and orientation dependent smoothness terms.

Resumo Limpo

work present unifi view markov random field recent propos continu tight convex relax multilabel assign imag plane relax far less bias toward grid geometri markov random field mrfs grid turn continu method nonlinear extens wellestablish local polytop mrf relax view result better understand tight convex relax discret set obtain wider rang optim method now applic find minim tight formul propos two method improv effici minim one use weaker effici continu inspir approach initi gradual refin energi necessari one reformul dual energi enabl smooth approxim use effici optim demonstr util propos minim scheme numer experi final general under energi formul isotrop metric smooth cost arbitrari nonmetr orient depend smooth term

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