Segmentation of brain tumors in 4D MR images using the hidden Markov model

Tumor size is an objective measure that is used to evaluate the effectiveness of anticancer agents. Responses to therapy are categorized as complete response, partial response, stable disease and progressive disease. Implicit in this scheme is the change in the tumor over time; however, most tumor segmentation algorithms do not use temporal information. Here we introduce an automated method using probabilistic reasoning over both space and time to segment brain tumors from 4D spatio-temporal MRI data. The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results. Spatial coherence via a Markov Random Field was included in the 3D spatial model. Simulated images as well as patient images from three independent sources were used to validate this method. The sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone.     

A model-based hand gesture recognition system

This paper introduces a model-based hand gesture recognition system, which consists of three phases: feature extraction, training, and recognition. In the feature extraction phase, a hybrid technique combines the spatial (edge) and the temporal (motion) information of each frame to extract the feature images. Then, in the training phase, we use the principal component analysis (PCA) to characterize spatial shape variations and the hidden Markov models (HMM) to describe the temporal shape variations. A modified Hausdorff distance measurement is also applied to measure the similarity between the feature images and the pre-stored PCA models. The similarity measures are referred to as the possible observations for each frame. Finally, in recognition phase, with the pre-trained PCA models and HMM, we can generate the observation patterns from the input sequences, and then apply the Viterbi algorithm to identify the gesture. In the experiments, we prove that our method can recognize 18 different continuous gestures effectively. Received: 19 May 1999 / Accepted: 4 September 2000     

Multi-aspect target discrimination using hidden Markov models and neural networks

This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions.     

基于SVM-HMM混合模型的说话人确认

提出一个文本无关的说话人确认的算法。该算法将支持向量机（SVM）的输出通过Sigmoid函数和高斯模型转化为概率，并作为隐式马尔可夫模型（HMM）中各个隐状态的输出概率。由于HMM适于处理连续信号，SVM适于处理分类问题；同时，HMM更多地表达了类别内部的相似性，而SVM则很大程度上反映了类别间的差异，因而根据两者不同的侧重点，使其组合获得了很好的效果。     

延时HMM在基因剪接供体位点识别中的应用

为使得隐马尔可夫模型(HMM)能够处理非相邻可见符号之间的依赖关系,将延时机制引入标准的HMM中。该技术仅仅改变了高阶状态发射概率的计算。所有适用于HMM的算法基本保持不变。该文设计了一个一阶延时隐马尔可夫模型和一个一阶标准隐马尔可夫模型,将两者分别应用于水稻基因剪接供体位点的识别。识别结果显示,延时模型的判别能力在一定程度上优于标准模型。对那些特征很不符合的位点,延时模型给出了相对低得多的得分。     

Lumpable hidden Markov models-model reduction and reducedcomplexity filtering

This paper is concerned with filtering of hidden Markov processes (HMP) which possess (or approximately possess) the property of lumpability. This property is a generalization of the property of lumpability of a Markov chain which has been previously addressed by others. In essence, the property of lumpability means that there is a partition of the (atomic) states of the Markov chain into aggregated sets which act in a similar manner as far as the state dynamics and observation statistics are concerned. We prove necessary and sufficient conditions on the HMP for exact lumpability to hold. For a particular class of hidden Markov models (HMM), namely finite output alphabet models, conditions for lumpability of all HMP representable by a specified HMM are given. The corresponding optimal filter algorithms for the aggregated states are then derived. The paper also describes an approach to efficient suboptimal filtering for HMP which are approximately lumpable. By this we mean that the HMM generating the process may be approximated by a lumpable HMM. This approach involves directly finding a lumped HMM which approximates the original HMM well, in a matrix norm sense. An alternative approach for model reduction based on approximating a given HMM by an exactly lumpable HMM is also derived. This method is based on the alternating convex projections algorithm. Some simulation examples are presented which illustrate the performance of the suboptimal filtering algorithms     

Hidden Markov models with arbitrary state dwell-time distributions

A hidden Markov model (HMM) with a special structure that captures the ‘semi’-property of hidden semi-Markov models (HSMMs) is considered. The proposed model allows arbitrary dwell-time distributions in the states of the Markov chain. For dwell-time distributions with finite support the HMM formulation is exact while for those that have infinite support, e.g. the Poisson, the distribution can be approximated with arbitrary accuracy. A benefit of using the HMM formulation is that it is easy to incorporate covariates, trend and seasonal variation particularly in the hidden component of the model. In addition, the formulae and methods for forecasting, state prediction, decoding and model checking that exist for ordinary HMMs are applicable to the proposed class of models. An HMM with explicitly modeled dwell-time distributions involving seasonality is used to model daily rainfall occurrence for sites in Bulgaria.     

A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases

Abstract: Application of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non‐invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy‐C‐means/CHMM (95.12%).     

基于HMM和遗传神经网络的语音识别系统

本文提出了一种基于隐马尔可夫(HMM)和遗传算法优化的反向传播网络(GA-BP)的混合模型语音识别方法。该方法首先利用HMM对语音信号进行时序建模,并计算出语音对HMM的输出概率的评分,将得到的概率评分作为优化后反向传播网络的输入,得到分类识别信息,最后根据混合模型的识别算法作出识别决策。通过Matlab软件对已有的样本数据进行训练和测试。仿真结果表明,由于设计充分利用了HMM时间建模能力强和GA-BP神经网络分类能力强等特点,该混合模型比单纯的HMM具有更强的抗噪性,克服了神经网络的局部最优问题,大大提高了识别的速度,明显改善了语音识别系统的性能。     

基于隐马尔可夫模型的视频异常场景检测

视频技术的广泛应用带来海量的视频数据,仅依靠人力对监控视频中的异常进行检测是不太可能的。异常行为的自动化检测在公共安全等领域的地位极其重要。提出一种综合考虑目标特性和时空上下文的异常检测方法,该方法利用光流纹理图描述移动物体的刚性特征,建立基于隐马尔可夫模型HMM的时间上下文异常检测模型。在此基础上,提取异常目标的Radon特征,以支持向量机SVM的异常预分类结果为基础,通过HMM建立异常场景的空间上下文分类模型。该模型在公共数据集UCSD PED2上进行了实验验证,结果表明,本算法不仅在异常检测方面优于已有算法,而且还能给出异常分类。     

基于隐Markov模型的文本分类

把基于序列模型的隐Markov模型引入文本分类领域。把待分类文本描述成一系列状态演化的隐Markov过程,其中状态以特定的概率产生代表文本的特征项。用序列模式来描述文本类,文本序列通过与隐Markov模型的匹配,求出其对应状态序列和最大输出概率。比较各个文本类的结果,达到文本分类的目的。最后通过和简单向量算法,KNN,Naive Bayes分类算法的比较,说明本算法的在文本分类中的成功应用。     

大数据环境下移动对象自适应轨迹预测模型

已有的轨迹预测算法针对移动对象运动模式,使用数学模型进行交通流模拟,难以对路网中的移动对象进行准确的描述.为了解决这一问题,提出基于隐马尔可夫模型(hidden Markov model,简称HMM)的自适应轨迹预测模型SATP(self-adaptive trajectory prediction model based on HMM),对大数据环境下移动对象海量轨迹利用基于密度的聚类方法进行位置密度分区和高效分段处理,减少HMM的状态数量.根据输入轨迹自动选取参数组合,避免HMM模型中隐状态不连续、状态停留等问题.实验结果表明,SATP模型在实验中表现出较高的预测准确性,并维持较低的时间开销.针对速度随机改变的移动对象,其平均预测准确率为84.1%;相同情况下,平均高出朴素预测算法46.7%.     

Modeling Default Data Via an Interactive Hidden Markov Model

In this paper, we first introduce the use of an interactive hidden Markov model (IHMM) for modeling and analyzing default data in a sector. Under the IHMM, transitions of the hidden risk states of the sector depend on the observed number of bonds in the sector that default in the current time period. This incorporates the feedback effect of the number of defaults on the transitions of the hidden risk states. This feature seems to be more realistic and does not enjoy by the traditional HMMs. We then develop a “dynamic” version of the binomial expansion technique (BET) modulated by the IHMM for modeling the occurrence of defaults of bonds issued by firms in the same sector. Under the BET modulated by the IHMM, the number of bonds defaulting in each time period follows a Markov-modulated binomial distribution with the probability of defaulting of each bond depending on the states of the IHMM, which represent the hidden risk states of the sector. Efficient method will be presented for estimating the model parameters in the BET modulated by the IHMM. We shall compare the hidden risk state process extracted from the IHMM-modulated BET with that extracted from the BET modulated by HMM in order to illustrate the significance of the feedback effect using real data. We shall also present the estimation results for the BET modulated by the IHMM and compare them with those for the BET modulated by the HMM.     

Optimising dynamic graphical models for video content analysis

A key problem in video content analysis using dynamic graphical models is to learn a suitable model structure given observed visual data. We propose a completed likelihood AIC (CL-AIC) scoring function for solving the problem. CL-AIC differs from existing scoring functions in that it aims to optimise explicitly both the explanation and prediction capabilities of a model simultaneously. CL-AIC is derived as a general scoring function suitable for both static and dynamic graphical models with hidden variables. In particular, we formulate CL-AIC for determining the number of hidden states for a hidden Markov model (HMM) and the topology of a dynamically multi-linked HMM (DML-HMM). The effectiveness of CL-AIC on learning the optimal structure of a dynamic graphical model especially given sparse and noisy visual date is shown through comparative experiments against existing scoring functions including Bayesian information criterion (BIC), Akaike’s information criterion (AIC), integrated completed likelihood (ICL), and variational Bayesian (VB). We demonstrate that CL-AIC is superior to the other scoring functions in building dynamic graphical models for solving two challenging problems in video content analysis: (1) content based surveillance video segmentation and (2) discovering causal/temporal relationships among visual events for group activity modelling.     

Maximum confidence hidden markov modeling for face recognition

This paper presents a hybrid framework of feature extraction and hidden Markov modeling(HMM) for two-dimensional pattern recognition. Importantly, we explore a new discriminative training criterion to assure model compactness and discriminability. This criterion is derived from hypothesis test theory via maximizing the confidence of accepting the hypothesis that observations are from target HMM states rather than competing HMM states. Accordingly, we develop the maximum confidence hidden Markov modeling (MC-HMM) for face recognition. Under this framework, we merge a transformation matrix to extract discriminative facial features. The closed-form solutions to continuous-density HMM parameters are formulated. Attractively, the hybrid MC-HMM parameters are estimated under the same criterion and converged through the expectation-maximization procedure. From the experiments on FERET and GTFD facial databases, we find that the proposed method obtains robust segmentation in presence of different facial expressions, orientations, etc. In comparison with maximum likelihood and minimum classification error HMMs, the proposed MC-HMM achieves higher recognition accuracies with lower feature dimensions.     

SCALED SELF-ORGANIZING MAP – HIDDEN MARKOV MODEL ARCHITECTURE FOR BIOLOGICAL SEQUENCE CLUSTERING

The self-organizing hidden Markov model map (SOHMMM) introduces a hybrid integration of the self-organizing map (SOM) and the hidden Markov model (HMM). Its scaled, online gradient descent unsupervised learning algorithm is an amalgam of the SOM unsupervised training and the HMM reparameterized forward-backward techniques. In essence, with each neuron of the SOHMMM lattice, an HMM is associated. The image of an input sequence on the SOHMMM mesh is defined as the location of the best matching reference HMM. Model tuning and adaptation can take place directly from raw data, within an automated context. The SOHMMM can accommodate and analyze deoxyribonucleic acid, ribonucleic acid, protein chain molecules, and generic sequences of high dimensionality and variable lengths encoded directly in nonnumerical/symbolic alphabets. Furthermore, the SOHMMM is capable of integrating and exploiting latent information hidden in the spatiotemporal dependencies/correlations of sequences’ elements.     

A recurrent log-linearized Gaussian mixture network

Context in time series is one of the most useful and interesting characteristics for machine learning. In some cases, the dynamic characteristic would be the only basis for achieving a possible classification. A novel neural network, which is named "a recurrent log-linearized Gaussian mixture network (R-LLGMN)," is proposed in this paper for classification of time series. The structure of this network is based on a hidden Markov model (HMM), which has been well developed in the area of speech recognition. R-LLGMN can as well be interpreted as an extension of a probabilistic neural network using a log-linearized Gaussian mixture model, in which recurrent connections have been incorporated to make temporal information in use. Some simulation experiments are carried out to compare R-LLGMN with the traditional estimator of HMM as classifiers, and finally, pattern classification experiments for EEG signals are conducted. It is indicated from these experiments that R-LLGMN can successfully classify not only artificial data but real biological data such as EEG signals.     

Detecting unattended packages through human activity recognition and object association

This paper provides a novel approach to detect unattended packages in public venues. Different from previous works on this topic which are mostly limited to detecting static objects where no human is nearby, we provide a solution which can detect an unattended package with people in its close proximity but not its owners. Mimicking the human logic in detecting such an event, our decision-making is based on understanding human activity and the relationships between humans and packages. There are three main contributions from this paper. First, an efficient method is provided to online categorize moving objects into the predefined classes using the eigen-features and the support vector machines (SVM). Second, utilizing the classification results, a method is developed to recognize human activities with hidden Markov models (HMM) and decide the package ownership. Finally the unattended package detection is achieved by analyzing multiple object relationships: package ownership, spatial and temporal distance relationships.     

基于视觉注意模型和HMM的足球视频语义分析

HMM模型具有良好的适应性,可以自动学习,对预测随机时序数据性能良好。场景是足球视频的基本特征,场景的转换体现了足球视频的摄制、编辑模式,表现了足球视频的语义。提出了一种基于场景分析和HMM的视频语义分析框架,用于识别足球视频中的一些语义事件。为了克服以往基于主颜色和其他底层特征的视频场景分析中存在的较大误差,又提出基于视觉注意模型对足球视频中的场景进行分析。实验结果表明,基于场景分析和HMM的事件识别方法对足球视频中的任意球事件有良好的识别效果     

基于条件随机域的上下文人类动作识别

提出一种新的基于条件随机域和隐马尔可夫模型（HMM）的人类动作识别方法——HMCRF。目前已有的动作识别方法均使用隐马尔可夫模型及其变型,这些模型一个最突出的不足就是要求观察值相互独立。条件模型很容易表示上下文相关性,且可使用动态规划做到有效且精确的推论,它的参数可以通过凸函数优化训练得到。把条件图形模型应用于动作识别之上,并通过大量的实验表明,所提出的方法在识别正确率方面明显优于一般线性结构的CRF和HMM。