IEEE Trans Image Process - Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

Tópicos

{ model(3404) distribut(989) bayesian(671) }
{ method(2212) result(1239) propos(1039) }
{ framework(1458) process(801) describ(734) }
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Resumo

Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

Resumo Limpo

chain graph cg hybrid probabilist graphic model pgm capabl model heterogen relationship among random variabl far howev applic imag video analysi limit due lack principl learn infer method cg general topolog overcom limit introduc method extend convent chainlik cg model cg model general topolog associ method learn infer general cg model specif propos techniqu systemat construct general structur cg parameter model deriv joint probabl distribut perform joint paramet learn perform probabilist infer model demonstr util extend cg appli two challeng imag video analysi problem human activ recognit imag segment experiment result show improv perform extend cg model convent direct undirect pgms studi demonstr promis extend cg effect model infer complex realworld problem

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