Topic
Markov random field
About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.
Papers published on a yearly basis
Papers
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TL;DR: An algorithm utilizing Markov random field modeling for identifying lung regions in a digitized chest radiograph (DCR) using the technique of Iterated Conditional Modes to determine the optimal classification of each pixel in a DCR is presented.
Abstract: The authors present an algorithm utilizing Markov random field modeling for identifying lung regions in a digitized chest radiograph (DCR). Let x represent the classifications of each pixel in a DCR as either lung or nonlung. We model x as a realization of a spatially varying Markov random field. This model is developed utilizing spatial and textural information extracted from samples of lung and nonlung region-types in a training set of DCRs. With this model, the technique of Iterated Conditional Modes is used to determine the optimal classification of each pixel in a DCR. The algorithm’s ability to identify lung regions is evaluated on a testing set of DCRs. The algorithm performs well yielding a sensitivity of 90.7%±4.4%, a specificity of 97.2%±2.0%, and an accuracy of 94.8%±1.6%. In an attempt to gain insight into the meaning and level of the algorithm’s performance numbers, the results are compared to those of some easily implemented classification algorithms.
49 citations
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01 Nov 2004TL;DR: The results demonstrate that unexpected side effects depending on the neighborhood window size may have larger accuracy impact than the neighborhood relationship effects of the Markov random field.
Abstract: In this paper we present a Markov random field model based approach to filter spam. Our approach examines the importance of the neighborhood relationship (MRF cliques) among words in an email message for the purpose of spam classification. We propose and test several different theoretical bases for weighting schemes among corresponding neighborhood windows. Our results demonstrate that unexpected side effects depending on the neighborhood window size may have larger accuracy impact than the neighborhood relationship effects of the Markov random field.
49 citations
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TL;DR: An unsupervised segmentation strategy for textured images, based on a hierarchical model in terms of discrete Markov Random Fields, where the textures are modeled as Gaussian Gibbs Fields, while the image partition is modeled as a Markov Mesh Random Field.
49 citations
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TL;DR: In this paper, a simple algorithm for reconstructing the underlying graph defining a Markov random field on $n$ nodes and maximum degree $d$ given observations is presented. But it is not shown that the algorithm can reconstruct the generating graph with high probability under mild nondegeneracy conditions.
Abstract: Markov random fields are used to model high dimensional distributions in a number of applied areas. Much recent interest has been devoted to the reconstruction of the dependency structure from independent samples from the Markov random fields. We analyze a simple algorithm for reconstructing the underlying graph defining a Markov random field on $n$ nodes and maximum degree $d$ given observations. We show that under mild nondegeneracy conditions it reconstructs the generating graph with high probability using $\Theta(d \epsilon^{-2}\delta^{-4} \log n)$ samples, where $\epsilon,\delta$ depend on the local interactions. For most local interactions $\epsilon,\delta$ are of order $\exp(-O(d))$. Our results are optimal as a function of $n$ up to a multiplicative constant depending on $d$ and the strength of the local interactions. Our results seem to be the first results for general models that guarantee that the generating model is reconstructed. Furthermore, we provide explicit $O(n^{d+2} \epsilon^{-2}\delta...
49 citations
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TL;DR: Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show the final segmentation to be accurate and more consistent than those obtained by competing methods, highlighting the effectiveness of self consistency in quantifying expert reliability and accuracy of SSL in predicting missing labels.
49 citations