A probabilistic approach for foreground and shadow segmentation in monocular image sequences

This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori—Markov random field estimation is used to boost the spatial connectivity of segmented regions.     

Across-camera object tracking using a conditional random field model

The Journal of Supercomputing - To ensure safety, most public spaces now deploy monitoring systems. However, in most scenarios, the tracking operations of these monitoring systems are performed...     

A wavelet-based Markov random field segmentation model in segmenting microarray experiments

In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r²), and the concordance correlation (p_c) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r², and p_c (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.     

An integration scheme for image segmentation and labeling based onMarkov random field model

This paper presents a unified approach for the image understanding problem based on the Markov random field models. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process so that the erroneous segmentation and misinterpretation can be compensatedly recovered by continuous estimation of the unified energy function     

基于马尔可夫随机场模型的运动对象分割算法

提出了一种基于马尔可夫随机场(MRF)模型的运动分割算法,仅使用了压缩流中的运动矢量和块编码模式信息,可以在复杂场景下对运动对象有很好的分割效果.利用运动矢量量化的方法来对运动矢量进行预处理,对运动矢量进行马尔可夫建模,利用能量最小函数进行优化得到运动对象分割的效果.实验表明:与现有的方法相比,该方法可从复杂场景中更准确地对运动对象进行分割.     

A nonparametric Riemannian framework on tensor field with application to foreground segmentation

Background modeling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent research has focused on developing parametric methods on the tensor domain, e.g. mixture of Gaussians (GMM). However, in some scenarios, simple parametric models do not accurately explain the physical processes. Kernel density estimators (KDEs) have been successful to model, on Euclidean sample spaces, the nonparametric nature of complex, time varying, and non-static backgrounds. Founded on a mathematically rigorous KDE paradigm on general Riemannian manifolds recently proposed in the literature, we define a KDE specifically to operate on the tensor manifold in order to nonparametrically reformulate the existing tensor-based algorithms. We present a mathematically sound framework for nonparametric modeling on tensor field to foreground detection. We endow the tensor manifold with two well-founded Riemannian metrics, i.e. Affine-Invariant and Log-Euclidean. Theoretical aspects are presented and the metrics are compared experimentally. By inducing a space with a null curvature, the Log-Euclidean metric considerably simplifies the scheme, from a practical point of view, while maintaining the mathematical soundness and the excellent segmentation performance. Theoretic analysis and experimental results demonstrate the promise and effectiveness of this framework.     

Tool wear state recognition based on linear chain conditional random field model

Tool condition monitoring (TCM) system is paramount for guaranteeing the quality of workpiece and improving the efficiency of the machining process. To overcome the shortcomings of Hidden Markov Model (HMM) and improve the accuracy of tool wear recognition, a linear chain conditional random field (CRF) model is presented. As a global conditional probability model, the main characteristic of this method is that the estimation of the model parameters depends not only on the current feature vectors but also on the context information in the training data. Therefore, it can depict the interrelationship between the feature vectors and the tool wear states accurately. To test the effectiveness of the proposed method, acoustic emission data are collected under four kinds of tool wear state and seven statistical features are selected to realize the tool wear classification by using CRF and hidden Markov model (HMM) based pattern recognition method respectively. Moreover, k-fold cross validation method is utilized to estimate the generation error accurately. The analysis and comparison under different folds schemes show that the CRF model is more accurate for the classification of the tool wear state. Moreover, the stability and the training speed of the CRF classifier outperform the HMM model. This method casts some new lights on the tool wear monitoring especially in the real industrial environment.     

Learning multi-scale features for foreground segmentation

Pattern Analysis and Applications - Foreground segmentation algorithms aim at segmenting moving objects from the background in a robust way under various challenging scenarios....     

基于条件随机域CRF模型的文本信息抽取

为了抽取文本中的信息,在分析对比了4种统计建模原型后,选用条件随机域CRF建立抽取模型,提出了一种文本信息抽取的方法.该方法对文本分析后加标注,确定文本特征集,采用有限内存拟牛顿迭代方法L-BFGS算法估计CRF模型参数,根据训练学习得出的模型,实现科研论文数据集头部文本信息的抽取.实验结果表明,使用CRF模型的抽取准确率达到90%以上,远远高于使用HMM模型的抽取准确率.     

基于条件随机场的矿工检测方法

针对现有视频图像目标检测算法应用于矿工检测时检出率、定位准确率、检测效率等均较低的问题,提出了一种基于条件随机场的矿工检测方法。该方法包括矿工检测模型建立与矿工检测识别2部分。在模型建立阶段,提取若干样本图像的方向梯度直方图特征,并利用主成分分析法对特征进行降维处理;以条件随机场为框架进行感兴趣区域标志,以标定训练样本,并训练条件随机场模型参数。在检测识别阶段,提取待检测图像的方向梯度直方图特征,并对特征进行降维,采用训练得到的条件随机场模型,通过局部二元模式推断标定图像各子窗口,最终得到矿工所在区域。实验结果表明,该方法可准确地检测出矿工在图像中的位置。     

Action categorization with modified hidden conditional random field

In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.     

Multi-class segmentation of free-form online documents with tree conditional random fields

We present a new system for predicting the segmentation of online handwritten documents into multiple blocks, such as text paragraphs, tables, graphics, or mathematical expressions. A hierarchical representation of the document is adopted by aggregating strokes into blocks, and interactions between different levels are modeled in a tree Conditional Random Field. Features are extracted, and labels are predicted at each tree level with logistic classifiers, and Belief Propagation is adopted for optimal inference over the structure. Being fully trainable, the system is shown to properly handle difficult segmentation problems arising in unconstrained online note-taking documents, where no prior knowledge is available regarding the layout or the expected content. Our experiments show very promising results and allow to envision fully automatic segmentation of free-form online notes.     

Markov random field models for unsupervised segmentation oftextured color images

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation     

基于条件随机场的新浪微博情感倾向性研究

采用知网发布的应用词源,通过自定义新浪微博的常用短语,形成一个专用的分析词语集合,并利用机器学习算法——条件随机场算法进行情感分析.对比试验表明,相较于马尔科夫随机场算法,本文方法在对新浪微博情感倾向性分析评测中取得了较好的结果.     

Unsupervised texture segmentation using Markov random field models

The problem of unsupervised segmentation of textured images is considered. The only explicit assumption made is that the intensity data can be modeled by a Gauss Markov random field (GMRF). The image is divided into a number of nonoverlapping regions and the GMRF parameters are computed from each of these regions. A simple clustering method is used to merge these regions. The parameters of the model estimated from the clustered segments are then used in two different schemes, one being all approximation to the maximum a posterior estimate of the labels and the other minimizing the percentage misclassification error. The proposed approach is contrasted with the algorithm of S. Lakshamanan and H. Derin (1989), which uses a simultaneous parameter estimation and segmentation scheme. The results of the adaptive segmentation algorithm of Lakshamanan and Derin are compared with a simple nearest-neighbor classification scheme to show that if enough information is available, simple techniques could be used as alternatives to computationally expensive schemes     

Fully automatic person segmentation in unconstrained video using spatio-temporal conditional random fields

The segmentation of objects and people in particular is an important problem in computer vision. In this paper, we focus on automatically segmenting a person from challenging video sequences in which we place no constraint on camera viewpoint, camera motion or the movements of a person in the scene. Our approach uses the most confident predictions from a pose detector as a form of anchor or keyframe stick figure prediction which helps guide the segmentation of other more challenging frames in the video. Since even state of the art pose detectors are unreliable on many frames –especially given that we are interested in segmentations with no camera or motion constraints –only the poses or stick figure predictions for frames with the highest confidence in a localized temporal region anchor further processing. The stick figure predictions within confident keyframes are used to extract color, position and optical flow features. Multiple conditional random fields (CRFs) are used to process blocks of video in batches, using a two dimensional CRF for detailed keyframe segmentation as well as 3D CRFs for propagating segmentations to the entire sequence of frames belonging to batches. Location information derived from the pose is also used to refine the results. Importantly, no hand labeled training data is required by our method. We discuss the use of a continuity method that reuses learnt parameters between batches of frames and show how pose predictions can also be improved by our model. We provide an extensive evaluation of our approach, comparing it with a variety of alternative grab cut based methods and a prior state of the art method. We also release our evaluation data to the community to facilitate further experiments. We find that our approach yields state of the art qualitative and quantitative performance compared to prior work and more heuristic alternative approaches.     

Application of the conditional population-mixture model to image segmentation

The problem of image segmentation is considered in the context of a mixture of probability distributions. The segments fall into classes. A probability distribution is associated with each class of segment. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood to the resulting likelihood function. A numerical example is given. Choice of the number of classes, using Akaike's information criterion (AIC) for model identification, is illustrated.