全卷积网络结合改进的条件随机场循环神经网络用于SAR图像场景分类

传统合成孔径雷达（SAR）图像基于粗分割像素块提取相关特征,后接支持向量机（SVM）和马尔可夫随机场（MRF）或条件随机场（CRF）进行分类,该方法存在同一像素块内部不同类别像素的误差,而且只考虑邻近区域未充分用到全局信息和结构信息。故考虑基于像素点引入全卷积网络（FCN）,以ESAR卫星图像为样本,基于像素点级别构建卷积网络进行训练,得到各像素的初始类别分类概率。为了考虑全局像素类别的影响后接CRF-循环神经网络（CRF-RNN）,利用FCN得到的初始概率,结合CRF结构得到全局像素类别转移结果,之后进行RNN的迭代进一步优化实验结果。由于基于像素点和考虑了全局信息与结构信息,克服了传统分类的部分缺点,使正确率较传统SVM或CRF方法平均提高了约6.5个百分点。由于CRF-RNN的距离权重是用高斯核人为拟合的,不能随实际训练样本来改变和确定,故存在一定误差,针对该问题提出可训练的全图距离权重卷积网络来改进CRF-RNN,最终实验结果表明改进后方法的正确率较未改进的CRF-RNN又提高了1.04个百分点。     

基于时空兴趣点和概率潜动态条件随机场模型的 在线行为识别方法

针对在线行为连续序列的识别问题以及行为识别模型的稳定性问题,提出一种监控视频中基于概率潜动态条件随机场（PLDCRF）的在线行为识别方法。首先,应用时空兴趣点（STIP）对行为特征进行提取;再利用PLDCRF模型识别室内人体的活动状态。PLDCRF模型融合了隐含状态变量,能够构建姿态序列子结构,可以选取姿态之间的动态特征,并且直接标记出未分割序列;同时也可以正确地标记出行为间的转换过程,从而明显改善了行为识别的效果。隐含条件随机场（HCRF）、潜动态条件随机场（LDCRF）、潜动态条件神经场（LDCNF）以及PLDCRF模型对10种不同动作的识别率比较结果表明,所提PLDCRF模型对连续的行为序列的综合识别能力更强,并且有更好的稳定性。     

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。     

基于分层分数条件随机场的行为识别

针对隐条件随机场(HCRF)的实时性问题和隐动态条件随机场（LDCRF）行为转换时的标记偏差问题,提出了一种基于分层分数条件随机场(SFCRF)模型的行为识别算法。该算法改进了LDCRF,并提出分数标记的概念,将人体行为的完整性和有向性具体化。实验结果表明,该算法取得了比条件随机场(CRF)、HCRF和LDCRF更好的识别效果。     

ADR-SPLDA: Activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation

This paper presents ADR-SPLDA, an unsupervised model for human activity discovery and recognition in pervasive environments. The activities are encoded in sequences recorded by non-intrusive sensors placed at various locations in the environment. Our model studies the relationship between the activities and the sequential patterns extracted from the sequences. Activity discovery is formulated as an optimization problem in which sequences are modeled as probability distributions over activities, and activities are, in turn, modeled as probability distributions over sequential patterns. The optimization problem is solved by maximization of the likelihood of data. We present experimental results on real datasets gathered in smart homes where people perform various activities of daily living. The results obtained demonstrate the suitability of our model for activity discovery and characterization. Also, we empirically demonstrate the effectiveness of our model for activity recognition by comparing it with two of the widely used models reported in the literature, the Hidden Markov model and the Conditional Random Field model.     

基于条件随机场的连续运动识别技术

在体育运动识别中,过渡姿势的复杂多变性容易导致识别错误。针对此问题,本文提出一种基于条件随机场CRF和条件概率密度传播Condensation的连续运动识别算法。该算法采用"分段识别"的思想,首先利用Condensation估计动作边界,然后分割出片段输入到CRF对其进行识别。实验结果表明,本文算法能减少过渡姿势对识别正确性的不良影响,比传统单纯使用CRF算法具有更好的稳定性和识别正确率。     

空基多平台多传感器时间空间数据配准与目标跟踪

研究多个空中移动平台的时间空间数据配准与目标跟踪问题.首先给出空中移动平台传感器数据空间配准几何坐标转换算法;然后采用最小二乘法对多传感器异步测量数据进行时间配准;最后将目标的运动模型和传感器配准误差模型组合在同一个状态方程中,利用扩展Kalman滤波方程进行估计.Monte-Carlo仿真表明,该方法能同时有效地估计目标运动状态和传感器配准误差,比传统配准方法具有更快的收敛速度和更高的精度.     

中文Base NP识别: 错误驱动的组合分类器方法

本文采用一种新的错误驱动的组合分类器方法来实现中文Base NP识别。本文首先对中文和英文Base NP识别技术现状进行了简要分析和概述,明确了中文Base NP识别的任务,然后,基于前人的工作提出了错误驱动的组合分类器方法,其基本思路是: 通过对比两种不同类型的分类器—基于转化的方法和条件随机场方法的分类结果,再利用支持向量机学习其中的错误规律,对两分类器产生的不同结果进行纠错,从而达到提高系统整体性能的效果。我们在宾州中文树库转化得到的Base NP语料集上进行了Base NP识别交叉验证实验,与单独使用基于转化的方法、条件随机场方法以及支持向量机方法相比较,错误驱动的组合分类器方法的实验结果都有所提高,最佳结果F值达到了89.72%,相对于文中Base NP识别的其他方法,最大提高幅度为2.35%。     

基于“固结词串”实例的中文分词研究

近几年的中文分词研究中,基于条件随机场(CRF)模型的中文分词方法得到了广泛的关注。但是这种分词方法在处理歧义切分方面存在一定的问题。CRF虽然可以消除大部分原有的分词歧义,却会带来更多新的错误切分。该文尝试找到一种简单的、基于“固结词串”实例的机器学习方法解决分词歧义问题。实验结果表明,该方法可以简单有效的解决原有的分词歧义问题,并且不会产生更多新的歧义切分。     

Discriminative Learning for Dynamic State Prediction

We consider the problem of predicting a sequence of real-valued multivariate states that are correlated by some unknown dynamics, from a given measurement sequence. Although dynamic systems such as the State-Space Models are popular probabilistic models for the problem, their joint modeling of states and observations, as well as the traditional generative learning by maximizing a joint likelihood may not be optimal for the ultimate prediction goal. In this paper, we suggest two novel discriminative approaches to the dynamic state prediction: 1) learning generative state-space models with discriminative objectives and 2) developing an undirected conditional model. These approaches are motivated by the success of recent discriminative approaches to the structured output classification in discrete-state domains, namely, discriminative training of Hidden Markov Models and Conditional Random Fields (CRFs). Extending CRFs to real multivariate state domains generally entails imposing density integrability constraints on the CRF parameter space, which can make the parameter learning difficult. We introduce an efficient convex learning algorithm to handle this task. Experiments on several problem domains, including human motion and robot-arm state estimation, indicate that the proposed approaches yield high prediction accuracy comparable to or better than state-of-the-art methods.     

基于条件随机场的异常协议行为检测方法*

针对现有异常应用协议行为检测主要针对某种特定应用,缺乏通用性的问题,提出一种基于条件随机场的异常应用协议行为检测方法,从网络数据流中提取应用协议关键字及其时间间隔作为状态特征,同时考虑关键字的频率分布特征,应用条件随机场模型对协议行为进行建模,将偏离模型的协议行为判定为异常。相比于传统的基于隐马尔可夫模型建模方法,该方法不必对特征量作出严格的独立性假设,具有能够融合多特征的优势。实验结果表明,本文方法在检测协议异常时准确率高,误报率低。     

Handling Movement Epenthesis and Hand Segmentation Ambiguities in Continuous Sign Language Recognition Using Nested Dynamic Programming

We consider two crucial problems in continuous sign language recognition from unaided video sequences. At the sentence level, we consider the movement epenthesis (me) problem and at the feature level, we consider the problem of hand segmentation and grouping. We construct a framework that can handle both of these problems based on an enhanced, nested version of the dynamic programming approach. To address movement epenthesis, a dynamic programming (DP) process employs a virtual me option that does not need explicit models. We call this the enhanced level building (eLB) algorithm. This formulation also allows the incorporation of grammar models. Nested within this eLB is another DP that handles the problem of selecting among multiple hand candidates. We demonstrate our ideas on four American Sign Language data sets with simple background, with the signer wearing short sleeves, with complex background, and across signers. We compared the performance with Conditional Random Fields (CRF) and Latent Dynamic-CRF-based approaches. The experiments show more than 40 percent improvement over CRF or LDCRF approaches in terms of the frame labeling rate. We show the flexibility of our approach when handling a changing context. We also find a 70 percent improvement in sign recognition rate over the unenhanced DP matching algorithm that does not accommodate the me effect.     

采用CRF技术的军事情报术语自动抽取研究

针对军事情报领域,提出了一种基于条件随机场的术语抽取方法,该方法将领域术语抽取看作一个序列标注问题,将领域术语分布的特征量化作为训练的特征,利用CRF工具包训练出一个领域术语特征模板,然后利用该模板进行领域术语抽取。实验采用的训练语料来自“搜狐网络军事频道”的新闻数据,测试语料选取《现代军事》杂志2007年第1~8期的所有文章。实验取得了良好的结果,准确率为73.24%,召回率为69.57%,F-测度为71.36%,表明该方法简单易行,且具有领域通用性。     

基于Tri-training的评价单元识别

评价单元的识别是情感倾向性分析中重要的一步,但由于标注语料匮乏,大多数研究集中在用人工构建规则、模板来识别评价单元的方法上。为了减轻标注训练语料的工作,同时进一步挖掘未标记样本的信息,提出一种基于协同训练机制的评价单元识别算法,以利用少量的已标记样本和大量的未标记样本来提高识别性能。该算法利用Tri-training的思想,将支持向量机(SVM)、最大熵(MaxEnt)以及条件随机场(CRF)三个不同分类器组合成一个分类体系,对生成的评价单元候选集进行分类。将Tri-training的算法思想应用于实验来对比采用单一分类器的方法,结果表明,该算法能够有效地识别主观句中的评价单元。     

基于条件随机场的中医临床病历命名实体抽取

中医临床病历是中医重要的科研数据资源,但目前临床病历仍以文本为主要表达形式,对病历数据深入分析的前提是进行结构化信息抽取,而命名实体抽取是其基础性步骤。针对中医临床病历的命名实体,如症状、疾病和诱因等的抽取问题,通过手工标注的413份病历数据(以中文字为特征)与4类特征模版,将条件随机场(CRF)、隐马尔科夫模型(HMM)和最大熵马尔科夫模型(MEMM)用于中医病历命名实体抽取的实验,并进行比较分析。结果表明,结合合适的特征模版,CRF命名实体抽取方法取得了较好的性能,F1值的症状达到0.80,疾病名称达到0.74,诱因0.74。与HMM和MEMM相比,CRF有最高的准确率和召回率,是一种较为适用的中医临床病历命名实体抽取方法。     

Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition

An important aspect in designing interactive, action-based interfaces is reliably recognizing actions with minimal latency. High latency causes the system’s feedback to lag behind user actions and thus significantly degrades the interactivity of the user experience. This paper presents algorithms for reducing latency when recognizing actions. We use a latency-aware learning formulation to train a logistic regression-based classifier that automatically determines distinctive canonical poses from data and uses these to robustly recognize actions in the presence of ambiguous poses. We introduce a novel (publicly released) dataset for the purpose of our experiments. Comparisons of our method against both a Bag of Words and a Conditional Random Field (CRF) classifier show improved recognition performance for both pre-segmented and online classification tasks. Additionally, we employ GentleBoost to reduce our feature set and further improve our results. We then present experiments that explore the accuracy/latency trade-off over a varying number of actions. Finally, we evaluate our algorithm on two existing datasets.     

实体关系识别中长距离依赖问题的研究

实体关系识别是信息抽取中的关键步骤,传统的词袋模型受到长距离依赖问题的影响,在处理实体关系识别过程中的性能不佳.条件随机场具有灵活的特征表达能力,因此非常适合表示复杂的语言现象.但传统的Linear-Chain CRF仍然不能表示长距离依赖问题,而Skip-Chain CRF仅考虑了相同词的长距离依赖问题,并且由于计算过于复杂,因此很难进行扩展.本文提出了一种新型的全连通随机场模型,使用词的相似度来建立依赖关系和使用词的互信息来删除依赖关系,同时改进了词的相似度计算公式,使其能够表示距离依赖关系,从而在解决长距离语言约束问题上克服了以往统计学习模型的缺陷,并在计算强度上与Linear-Chain CRF大致相当,在实体关系识别中的性能超过了目前的Linear-Chain CRF和Skip-Chain CRF.     

CRF与规则相结合的地理空间命名实体识别

提出条件随机场(CRF)与规则相结合的地理空间命名实体识别方法.该方法以丰富的知识作为触发条件,用CRF对满足条件的片段作地名及机构名识别.识别出来的命名实体又被解构,CRF及知识用来进一步判断该命名实体是否表示事件发生地的地理空间信息.实验结果表明,统计与规则方法的结合以及解构算法有效提升了地理空间命名实体识别的性能...     

基于分层特征关联条件随机场的遥感图像分类

针对高分辨率遥感图像分类中空间上下文信息表达的难题,提出了一种新的多尺度条件随机场(CRF)模型。首先将图像内容表示成从细到粗三个超像素层:区域层、对象层、场景层,并将超像素特征逐层关联形成特征向量;再利用支持向量机(SVM)定义CRF关联势函数,利用相邻超像素特征对比度加权的Potts模型定义CRF交互势函数,最后形成一个分层特征关联的多尺度SVM-CRF模型。以Quickbird遥感图像中两个复杂场景为测试数据对该模型的分类有效性进行了验证,结果表明:该模型比基于上述三个超像素层的单尺度SVM-CRF模型分类精度分别平均提高了2.68%、1.66%、3.75%,而且分类时耗时较少。     

Sensor-Based Abnormal Human-Activity Detection

With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particular important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between abnormality detection rate and false alarm rate, and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting.