介绍了基于半条件随机域(semi-Markov conditional random fields,简称semi-CRFs)模型的百科全书文本段落划分方法.为了克服单纯的HMM模型和CRF模型的段落类型重复问题,以经过整理的HMM模型状态的后验分布为基本依据,使用了基于词汇语...介绍了基于半条件随机域(semi-Markov conditional random fields,简称semi-CRFs)模型的百科全书文本段落划分方法.为了克服单纯的HMM模型和CRF模型的段落类型重复问题,以经过整理的HMM模型状态的后验分布为基本依据,使用了基于词汇语义本体知识库的段落开始特征以及针对特定段落类型的提示性特征来进一步适应目标文本的特点.实验结果表明,该划分方法可以综合利用各种不同类型的信息,比较适合百科全书文本的段落结构,可以取得比单纯的HMM模型和CRF模型更好的性能.展开更多
This paper aims to address the problem of modeling human behavior patterns captured in surveil- lance videos for the application of online normal behavior recognition and anomaly detection. A novel framework is develo...This paper aims to address the problem of modeling human behavior patterns captured in surveil- lance videos for the application of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior modeling and online anomaly detection without the need for manual labeling of the training data set. The framework consists of the following key components. 1） A compact and effective behavior representation method is developed based on spatial-temporal interest point detection. 2） The natural grouping of behavior patterns is determined through a novel clustering algorithm, topic hidden Markov model （THMM） built upon the existing hidden Markov model （HMM） and latent Dirichlet allocation （LDA）, which overcomes the current limitations in accuracy, robustness, and computational efficiency. The new model is a four- level hierarchical Bayesian model, in which each video is modeled as a Markov chain of behavior patterns where each behavior pattern is a distribution over some segments of the video. Each of these segments in the video can be modeled as a mixture of actions where each action is a distribution over spatial-temporal words. 3） An online anomaly measure is introduced to detect abnormal behavior, whereas normal behavior is recognized by runtime accumulative visual evidence using the likelihood ratio test （LRT） method. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.展开更多
文摘介绍了基于半条件随机域(semi-Markov conditional random fields,简称semi-CRFs)模型的百科全书文本段落划分方法.为了克服单纯的HMM模型和CRF模型的段落类型重复问题,以经过整理的HMM模型状态的后验分布为基本依据,使用了基于词汇语义本体知识库的段落开始特征以及针对特定段落类型的提示性特征来进一步适应目标文本的特点.实验结果表明,该划分方法可以综合利用各种不同类型的信息,比较适合百科全书文本的段落结构,可以取得比单纯的HMM模型和CRF模型更好的性能.
文摘This paper aims to address the problem of modeling human behavior patterns captured in surveil- lance videos for the application of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior modeling and online anomaly detection without the need for manual labeling of the training data set. The framework consists of the following key components. 1） A compact and effective behavior representation method is developed based on spatial-temporal interest point detection. 2） The natural grouping of behavior patterns is determined through a novel clustering algorithm, topic hidden Markov model （THMM） built upon the existing hidden Markov model （HMM） and latent Dirichlet allocation （LDA）, which overcomes the current limitations in accuracy, robustness, and computational efficiency. The new model is a four- level hierarchical Bayesian model, in which each video is modeled as a Markov chain of behavior patterns where each behavior pattern is a distribution over some segments of the video. Each of these segments in the video can be modeled as a mixture of actions where each action is a distribution over spatial-temporal words. 3） An online anomaly measure is introduced to detect abnormal behavior, whereas normal behavior is recognized by runtime accumulative visual evidence using the likelihood ratio test （LRT） method. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.
