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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
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Journal ArticleDOI
TL;DR: An unsupervised segmentation approach to classification of multispectral image is suggested here in Markov random field (MRF) frame work and the findings are found to be encouraging.
Abstract: An unsupervised segmentation approach to classification of multispectral image is suggested here in Markov random field (MRF) frame work. This work generalizes the work of Sarkar et al. (2000) on gray value images for multispectral images and is extended for landuse classification. The essence of this approach is based on capturing intrinsic characters of tonal and textural regions of any multispectral image. The approach takes an initially oversegmented image and the original. multispectral image as the input and defines a MRF over region adjacency graph (RAG) of the initially segmented regions. Energy function minimization associated with the MRF is carried out by applying a multivariate statistical test. A cluster validation scheme is outlined after obtaining optimal segmentation. Quantitative evaluation of classification accuracy of test data for three illustrations are shown and compared with conventional maximum likelihood procedure. Comparison of the proposed methodology with a recent work of texture segmentation in the literature has also been provided. The findings of the proposed method are found to be encouraging.

93 citations

Journal ArticleDOI
TL;DR: This work proposes a fully automated method for prostate segmentation using random forests (RFs) and graph cuts, and shows that inclusion of the context and semantic information contributes to higher segmentation accuracy than other methods.
Abstract: We propose a fully automated method for prostate segmentation using random forests (RFs) and graph cuts. A volume of interest (VOI) is automatically selected using supervoxel segmentation, and its subsequent classification using image features and RF classifiers. The VOIs probability map is generated using image and context features, and a second set of RF classifiers. The negative log-likelihood of the probability maps acts as the penalty cost in a second-order Markov random field cost function. Semantic information from the second set of RF classifiers is an important measure of each feature to the classification task, which contributes to formulating the smoothness cost. The cost function is optimized using graph cuts to get the final segmentation of the prostate. With average dice metric (DM) (on the training set) and DM (on the test set), our experimental results show that inclusion of the context and semantic information contributes to higher segmentation accuracy than other methods.

93 citations

Journal ArticleDOI
Yu Guan1, Wei Chen1, Xiao Liang1, Zi'ang Ding1, Qunsheng Peng1 
TL;DR: This work proposes an iterative energy minimization framework for interactive image matting and demonstrates that the energy‐driven scheme can be extended to video matting, with which the spatio‐temporal smoothness is faithfully preserved.
Abstract: We propose an iterative energy minimization framework for interactive image matting. Our approach is easy in the sense that it is fast and requires only few user-specified strokes for marking the foreground and background. Beginning with the known region, we model the unknown region as a Markov Random Field (MRF) and formulate its energy in each iteration as the combination of one data term and one smoothness term. By automatically adjusting the weights of both terms during the iterations, the first-order continuous and feature-preserving result is rapidly obtained with several iterations. The energy optimization can be further performed in selected local regions for refined results. We demonstrate that our energy-driven scheme can be extended to video matting, with which the spatio-temporal smoothness is faithfully preserved. We show that the proposed approach outperforms previous methods in terms of both the quality and performance for quite challenging examples.

93 citations

Journal ArticleDOI
TL;DR: A new message passing scheme named tile-based BP that reduces the memory and bandwidth to a fraction of the ordinary BP algorithms without performance degradation by splitting the MRF into many tiles and only storing the messages across the neighboring tiles is proposed.
Abstract: Loopy belief propagation (BP) is an effective solution for assigning labels to the nodes of a graphical model such as the Markov random field (MRF), but it requires high memory, bandwidth, and computational costs. Furthermore, the iterative, pixel-wise, and sequential operations of BP make it difficult to parallelize the computation. In this paper, we propose two techniques to address these issues. The first technique is a new message passing scheme named tile-based BP that reduces the memory and bandwidth to a fraction of the ordinary BP algorithms without performance degradation by splitting the MRF into many tiles and only storing the messages across the neighboring tiles. The tile-wise processing also enables data reuse and pipeline, resulting in efficient hardware implementation. The second technique is an O(L) fast message construction algorithm that exploits the properties of robust functions for parallelization. We apply these two techniques to a very large-scale integration circuit for stereo matching that generates high-resolution disparity maps in near real-time. We also implement the proposed schemes on graphics processing unit (GPU) which is four-time faster than standard BP on GPU.

93 citations

Proceedings ArticleDOI
13 Jun 2010
TL;DR: The first result establishes a set of necessary conditions on n(p, d) for any recovery method to consistently estimate the underlying graph, and the second result provides necessary conditions for any decoder to produce an estimate of the true inverse covariance matrix T satisfying ‖ Θ̂-Θ ‖ < δin the elementwise ℓ∞-norm.
Abstract: The problem of graphical model selection is to estimate the graph structure of an unknown Markov random field based on observed samples from the graphical model. For Gaussian Markov random fields, this problem is closely related to the problem of estimating the inverse covariance matrix of the underlying Gaussian distribution. This paper focuses on the information-theoretic limitations of Gaussian graphical model selection and inverse covariance estimation in the high-dimensional setting, in which the graph size p and maximum node degree d are allowed to grow as a function of the sample size n. Our first result establishes a set of necessary conditions on n(p, d) for any recovery method to consistently estimate the underlying graph. Our second result provides necessary conditions for any decoder to produce an estimate Θ Θ of the true inverse covariance matrix T satisfying ‖ Θ-Θ ‖ ∞ -norm (which implies analogous results in the Frobenius norm as well). Combined with previously known sufficient conditions for polynomial-time algorithms, these results yield sharp characterizations in several regimes of interest.

93 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
202330
2022128
202196
2020173
2019204