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Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields 被引量:5
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作者 Siddhartha Shakya John McCall 《International Journal of Automation and computing》 EI 2007年第3期262-272,共11页
This paper presents a Markov random field (MRP) approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general he... This paper presents a Markov random field (MRP) approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields (DEUM). DEUM is a subclass of estimation of distribution algorithms (EDAs) where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph. The focus of this paper will be on describing the three main characteristics of DEUM framework, which distinguishes it from the traditional EDA. They are: 1) use of MRF models, 2) fitness modeling approach to estimating the parameter of the model and 3) Monte Carlo approach to sampling from the model. 展开更多
关键词 Estimation of distribution algorithms evolutionary algorithms fitness modeling markov random fields Gibbs distri-bution.
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Reservoir lithology stochastic simulation based on Markov random fields 被引量:2
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作者 梁玉汝 王志忠 郭建华 《Journal of Central South University》 SCIE EI CAS 2014年第9期3610-3616,共7页
Markov random fields(MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass re... Markov random fields(MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass relationships. While, many relative studies were based on Markov chain, not MRF, and using Markov chain model for 3D reservoir stochastic simulation has always been the difficulty in reservoir stochastic simulation. MRF was proposed to simulate type variables(for example lithofacies) in this work. Firstly, a Gibbs distribution was proposed to characterize reservoir heterogeneity for building 3-D(three-dimensional) MRF. Secondly, maximum likelihood approaches of model parameters on well data and training image were considered. Compared with the simulation results of MC(Markov chain), the MRF can better reflect the spatial distribution characteristics of sand body. 展开更多
关键词 stochastic modeling markov random fields training image Monte Carlo simulation
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Fusing PLSA model and Markov random fields for automatic image annotation 被引量:1
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作者 田东平 Zhao Xiaofei Shi Zhongzhi 《High Technology Letters》 EI CAS 2014年第4期409-414,共6页
A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to esti... A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches. 展开更多
关键词 automatic image annotation probabilistic latent semantic analysis (PLSA) expectation maximization markov random fields (MRF) image retrieval
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THE DECISION OF THE OPTIMAL PARAMETERS IN MARKOV RANDOM FIELDS OF IMAGES BY GENETIC ALGORITHM
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作者 Zheng Zhaobao Zheng Hong 《Geo-Spatial Information Science》 2000年第3期14-18,共5页
This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is propo... This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experiments on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory. 展开更多
关键词 GENETIC algorithm markov random field PARAMETER OPTIMUM TEXTURE cl assification
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Segmentation of MS lesions using entropy-based EM algorithm and Markov random fields 被引量:1
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作者 Ahmad Bijar Mahdi Mohamad Khanloo +1 位作者 Antonio Penalver Benavent Rasoul Khayati 《Journal of Biomedical Science and Engineering》 2011年第8期552-561,共10页
This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with... This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with three kernels as cerebrospinal fluid (CSF), normal tissue and Multiple Sclerosis lesions. To estimate this model, an automatic Entropy based EM algorithm is used to find the best estimated Model. Then, Markov random field (MRF) model and EM algorithm are utilized to obtain and upgrade the class conditional probability density function and the apriori probability of each class. After estimation of Model parameters and apriori probability, brain tissues are classified using bayesian classification. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here. 展开更多
关键词 Gaussian Mixture Model EM ENTROPY markov random field Multiple Sclerosis
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Modified Maximum Likelihood Estimation of the Spatial Resolution for the Elliptical Gamma Camera SPECT Imaging Using Binary Inhomogeneous Markov Random Fields Models
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作者 Stelios Zimeras 《Advances in Computed Tomography》 2013年第2期68-75,共8页
In this work a complete approach for estimation of the spatial resolution for the gamma camera imaging based on the [1] is analyzed considering where the body distance is detected (close or far way). The organ of inte... In this work a complete approach for estimation of the spatial resolution for the gamma camera imaging based on the [1] is analyzed considering where the body distance is detected (close or far way). The organ of interest most of the times is not well defined, so in that case it is appropriate to use elliptical camera detection instead of circular. The image reconstruction is presented which allows spatially varying amounts of local smoothing. An inhomogeneous Markov random field (M.r.f.) model is described which allows spatially varying degrees of smoothing in the reconstructions and a re-parameterization is proposed which implicitly introduces a local correlation structure in the smoothing parameters using a modified maximum likelihood estimation (MLE) denoted as one step late (OSL) introduced by [2]. 展开更多
关键词 markov random fields INHOMOGENEOUS MODELS Image RECONSTRUCTIONS Single PHOTON Emission
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Prestack inversion based on anisotropic Markov random field-maximum posterior probability inversion and its application to identify shale gas sweet spots 被引量:3
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作者 王康宁 孙赞东 董宁 《Applied Geophysics》 SCIE CSCD 2015年第4期533-544,628,共13页
Economic shale gas production requires hydraulic fracture stimulation to increase the formation permeability. Hydraulic fracturing strongly depends on geomechanical parameters such as Young's modulus and Poisson's r... Economic shale gas production requires hydraulic fracture stimulation to increase the formation permeability. Hydraulic fracturing strongly depends on geomechanical parameters such as Young's modulus and Poisson's ratio. Fracture-prone sweet spots can be predicted by prestack inversion, which is an ill-posed problem; thus, regularization is needed to obtain unique and stable solutions. To characterize gas-bearing shale sedimentary bodies, elastic parameter variations are regarded as an anisotropic Markov random field. Bayesian statistics are adopted for transforming prestack inversion to the maximum posterior probability. Two energy functions for the lateral and vertical directions are used to describe the distribution, and the expectation-maximization algorithm is used to estimate the hyperparameters of the prior probability of elastic parameters. Finally, the inversion yields clear geological boundaries, high vertical resolution, and reasonable lateral continuity using the conjugate gradient method to minimize the objective function. Antinoise and imaging ability of the method were tested using synthetic and real data. 展开更多
关键词 shale gas/oil sweet spot prestack inversion markov random field
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Fast segmentation approach for SAR image based on simple Markov random field 被引量:8
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作者 Xiaogang Lei Ying Li Na Zhao Yanning Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第1期31-36,共6页
Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field (MRF) for S... Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field (MRF) for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach. 展开更多
关键词 SAR image segmentation simple markov random field coarse segmentation maximum a posterior iterated condition mode.
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Image Dehazing by Incorporating Markov Random Field with Dark Channel Prior 被引量:3
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作者 XU Hao TAN Yibo +1 位作者 WANG Wenzong WANG Guoyu 《Journal of Ocean University of China》 SCIE CAS CSCD 2020年第3期551-560,共10页
As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy gro... As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given. 展开更多
关键词 image dehazing dark channel prior markov random field image segmentation
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A User Participation Behavior Prediction Model of Social Hotspots Based on Influence and Markov Random Field 被引量:2
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作者 Yunpeng Xiao Jiawei Lai Yanbing Liu 《China Communications》 SCIE CSCD 2017年第5期145-159,共15页
Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user ... Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics. 展开更多
关键词 social network hotspot topic behavior prediction markov random field influence factor
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Coherence-coefficient-based Markov random field approach for building segmentation from high-resolution SAR images 被引量:3
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作者 QIAN Qian WANG Bing-nan +2 位作者 XIANG Mao-sheng FU Xi-kai JIANG Shuai 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2019年第3期226-235,共10页
Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values chang... Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering. 展开更多
关键词 building segmentation high-resolution synthetic aperture rader (SAR) image markov random field (MRF) coherence coefficient
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A New Encryption-Then-Compression Scheme on Gray Images Using the Markov Random Field 被引量:1
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作者 Chuntao Wang Yang Feng +2 位作者 Tianzheng Li Hao Xie Goo-Rak Kwon 《Computers, Materials & Continua》 SCIE EI 2018年第7期107-121,共15页
Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,... Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus proposed.After preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,respectively.Thus,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D Markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery. 展开更多
关键词 Encryption-then-compression compressing encrypted image markov random field compression efficiency factor graph.
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An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field 被引量:1
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作者 Qin Wan Xiaolin Zhu +3 位作者 Yueping Xiao Jine Yan Guoquan Chen Mingui Sun 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第7期129-149,共21页
Detecting moving objects in the stationary background is an important problem in visual surveillance systems.However,the traditional background subtraction method fails when the background is not completely stationary... Detecting moving objects in the stationary background is an important problem in visual surveillance systems.However,the traditional background subtraction method fails when the background is not completely stationary and involves certain dynamic changes.In this paper,according to the basic steps of the background subtraction method,a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the Markov random field.Concretely,the contributions are as follows:1)A new nonparametric strategy is utilized to model the background,based on an improved kernel density estimation;this approach uses an adaptive bandwidth,and the fused features combine the colours,gradients and positions.2)A Markov random field method based on this adaptive background model via the constraint of the spatial context is proposed to extract objects.3)The posterior function is maximized efficiently by using an improved ant colony system algorithm.Extensive experiments show that the proposed method demonstrates a better performance than many existing state-of-the-art methods. 展开更多
关键词 Object detection non-parametric method markov random field
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Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain 被引量:1
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作者 Hong Fan Yiman Sun +3 位作者 Xiaojuan Zhang Chengcheng Zhang Xiangjun Li Yi Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2021年第7期655-667,共13页
To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov rand... To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation. 展开更多
关键词 undecimated dual-tree complex wavelet MR image segmentation multi-resolution markov random field model
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CLOUD IMAGE DETECTION BASED ON MARKOV RANDOM FIELD 被引量:1
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作者 Xu Xuemei Guo Yuanwei Wang Zhenfei 《Journal of Electronics(China)》 2012年第3期262-270,共9页
In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the bas... In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise. 展开更多
关键词 Cloud image detection markov random field (MRF) Belief Propagation (BP) Iterated Conditional Modes (ICM)
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IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES
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作者 NASSIR H.SALMAN(纳瑟) +2 位作者 LIU Chong-qing (刘重庆) 《Journal of Shanghai Jiaotong university(Science)》 EI 2002年第1期36-41,共6页
This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial esti... This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image. 展开更多
关键词 markov random field(MRF) WATERSHED algorithm K-means edge detection IMAGE segmentation IMAGE analysis
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ON MARKOV RANDOM FIELD MODELS FOR SEGMENTATION OF NOISY IMAGES
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作者 Kuang Jinyu Zhu Junxiu (Department of Radio-Electronics, Beijing Normal University, Beijing 100875) 《Journal of Electronics(China)》 1996年第1期31-39,共9页
Markov random field(MRF) models for segmentation of noisy images are discussed. According to the maximum a posteriori criterion, a configuration of an image field is regarded as an optimal estimate of the original sce... Markov random field(MRF) models for segmentation of noisy images are discussed. According to the maximum a posteriori criterion, a configuration of an image field is regarded as an optimal estimate of the original scene when its energy is minimized. However, the minimum energy configuration does not correspond to the scene on edges of a given image, which results in errors of segmentation. Improvements of the model are made and a relaxation algorithm based on the improved model is presented using the edge information obtained by a coarse-to-fine procedure. Some examples are presented to illustrate the applicability of the algorithm to segmentation of noisy images. 展开更多
关键词 markov random field Gibbs distribution EDGE detection RELAXATION algorithm Image SEGMENTATION
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Elitist Reconstruction Genetic Algorithm Based on Markov Random Field for Magnetic Resonance Image Segmentation
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作者 Xin-Yu Du,Yong-Jie Li,Cheng Luo,and De-Zhong Yao the School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu 610054,China 《Journal of Electronic Science and Technology》 CAS 2012年第1期83-87,共5页
In this paper, elitist reconstruction genetic algorithm (ERGA) based on Markov random field (MRF) is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every ... In this paper, elitist reconstruction genetic algorithm (ERGA) based on Markov random field (MRF) is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms. 展开更多
关键词 Elitist reconstruction genetic algorithm image segmentation markov random field.
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Semi-supervised classification based on Markov Random Field and Robust Error Function
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作者 LIN Qing SHAN Ping-ping +1 位作者 WANG Shi-tong ZHAN Yong-zhao 《通讯和计算机(中英文版)》 2009年第4期1-5,共5页
关键词 半管理 markov随机场 误差函数 能量函数
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Specular Detection and Removal for a Grayscale Image Based on the Markov Random Field
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作者 Fang Yin Tiantian Chen +2 位作者 Rui Wu Ziru Fu Xiaoyang Yu 《国际计算机前沿大会会议论文集》 2016年第1期165-167,共3页
Specular detection and removal has been a hot topic in the field of computer vision. Most of the existing methods are mainly for color images, but grayscale images are widely used. For a single grayscale image with on... Specular detection and removal has been a hot topic in the field of computer vision. Most of the existing methods are mainly for color images, but grayscale images are widely used. For a single grayscale image with only intensity information, highlight detection and removal becomes a difficult issue. To solve this problem, the single grayscale image highlight detection and removal method based on Markov random field is presented. Each reflection component modeling is estimated by geometric relation of surface normal in diffuse and specular reflection component in the framework of Markov random field. Their maximum a posteriori estimation is calculated under Bayesian formula and highlight area is detected. Finally, image inpainting method based on the BSCB model removes highlights. Experiment reveals that this method can effectively detect grayscale image specular reflection area, improve highlight areas the repair rate. 展开更多
关键词 COMPUTER VISION Specular DETECTION markov random field
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