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Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 516 (18 self)
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. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling
Representationbased classifications with Markov random field model for hyperspectral urban data
"... with Markov random field model for ..."
Strong Markov Random Field Model
"... The strong Markov random field (MRF) model is a submodel of the more general MRFGibbs model. The strongMRF model defines a system whereby not only is the field Markovian with respect to a defined neighbourhood, but all subneighbourhoods also define a Markovian system. A checkerboard pattern is a ..."
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Cited by 10 (0 self)
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The strong Markov random field (MRF) model is a submodel of the more general MRFGibbs model. The strongMRF model defines a system whereby not only is the field Markovian with respect to a defined neighbourhood, but all subneighbourhoods also define a Markovian system. A checkerboard pattern
A Markov random field model for term dependencies
"... This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases. W ..."
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Cited by 289 (55 self)
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This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases
Subpixel Land Cover Mapping Based on Markov Random Field Models
, 2003
"... Subpixel land cover mapping based on Markov random field models ..."
Markov Random Field Models in Image Processing
, 1995
"... INTRODUCTION Markov random field models have become useful in several areas of image processing. The success of Markov random fields (MRFs) can be attributed to the fact that they give rise to good, flexible, stochastic image models. The goal of image modeling is to find an adequate representation o ..."
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Cited by 5 (1 self)
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INTRODUCTION Markov random field models have become useful in several areas of image processing. The success of Markov random fields (MRFs) can be attributed to the fact that they give rise to good, flexible, stochastic image models. The goal of image modeling is to find an adequate representation
Statistical Dependence in Markov Random Field Models
, 2007
"... Statistical models based on Markov random fields present a flexible means for modeling statistical dependencies in a variety of situations including, but not limited to, spatial problems with observations on a lattice. The simplest of such models, sometimes called “automodels ” are formulated fro ..."
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Cited by 1 (1 self)
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Statistical models based on Markov random fields present a flexible means for modeling statistical dependencies in a variety of situations including, but not limited to, spatial problems with observations on a lattice. The simplest of such models, sometimes called “automodels ” are formulated
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 639 (15 self)
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based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
On Block Updating in Markov Random Field Models For . . .
 SCANDINAVIAN JOURNAL OF STATISTICS
, 2002
"... Gaussian Markov random field (GMRF) models are commonlyufz to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly singlesiteuinglealgorithms have been considered. However, convergence and mixing properties ofsuD algorithms can be extremely ..."
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Cited by 85 (8 self)
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Gaussian Markov random field (GMRF) models are commonlyufz to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly singlesiteuinglealgorithms have been considered. However, convergence and mixing properties ofsuD algorithms can be extremely
Automatic Feature Selection in the Markov Random Field Model for Information Retrieval
 In Proceedings of CIKM’07
, 2007
"... Previous applications of the Markov random field model for information retrieval have used manually chosen features. However, it is often difficult or impossible to know, a priori, the best set of features to use for a given task or data set. Therefore, there is a need to develop automatic feature s ..."
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Cited by 39 (6 self)
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Previous applications of the Markov random field model for information retrieval have used manually chosen features. However, it is often difficult or impossible to know, a priori, the best set of features to use for a given task or data set. Therefore, there is a need to develop automatic feature
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