可变场所的异常行为识别方法

在视觉分析中,人的同一动作在不同场景下会有截然不同的理解.为了判断在不同场景中行为是否为异常,在监控系统中使用双层词包模型来解决这个问题.把视频信息放在第1层包中,把场景动作文本词放在第2层包中.视频由一系列时空兴趣点组成的时空词典表示,动作性质由在指定场景下的动作文本词集合来确定.使用潜在语义分析概率模型(pLSA)不但能自动学习时空词的概率分布,找到与之对应的动作类别,也能在监督情况下学习在规定场景下运动文本词概率分布并区分出对应异常或正常行动结果.经过训练学习后,该算法可以识别新视频在相应场景下行为的异常或正常.     

Graph-based clustering and ranking for diversified image search

In this paper, we consider the problem of clustering and re-ranking web image search results so as to improve diversity at high ranks. We propose a novel ranking framework, namely cluster-constrained conditional Markov random walk (CCCMRW), which has two key steps: first, cluster images into topics, and then perform Markov random walk in an image graph conditioned on constraints of image cluster information. In order to cluster the retrieval results of web images, a novel graph clustering model is proposed in this paper. We explore the surrounding text to mine the correlations between words and images and therefore the correlations are used to improve clustering results. Two kinds of correlations, namely word to image and word to word correlations, are mainly considered. As a standard text process technique, tf-idf method cannot measure the correlation of word to image directly. Therefore, we propose to combine tf-idf method with a novel feature of word, namely visibility, to infer the word-to-image correlation. By latent Dirichlet allocation model, we define a topic relevance function to compute the weights of word-to-word correlations. Taking word to image correlations as heterogeneous links and word-to-word correlations as homogeneous links, graph clustering algorithms, such as complex graph clustering and spectral co-clustering, are respectively used to cluster images into topics in this paper. In order to perform CCCMRW, a two-layer image graph is constructed with image cluster nodes as upper layer added to a base image graph. Conditioned on the image cluster information from upper layer, Markov random walk is constrained to incline to walk across different image clusters, so as to give high rank scores to images of different topics and therefore gain the diversity. Encouraging clustering and re-ranking outputs on Google image search results are reported in this paper.     

教学视频的文本语义镜头分割和标注

为了对教学视频这一专门类别视频进行自动标注,本文首先提取视频中的字幕信息,通过文本预处理后,使用视频中的字幕文本信息内容结合潜在狄利克雷分布（Latent Dirichlet allocation,LDA）主题模型方法获得视频镜头在主题上的概率分布,通过计算主题概率分布差异,进行语义层面镜头分割。然后以镜头为样本,使用安全的半监督支持向量机（Safe semi-supervised support vector machine,S4VM）方法,通过少量的标注镜头样本,完成对未标注镜头的自动标注。实验结果表明,本文方法利用字幕文本信息和LDA模型,有效完成了视频的语义镜头分割,不仅可以对镜头完成标注,而且可以对整个视频进行关键词标注。     

基于峭度图像的视频指纹算法

为解决网络视频的非法拷贝问题,提出一种基于峭度图像的视频指纹算法。对视频片段进行预处理后,利用均匀分布的随机变量提取关键帧以及关键帧的峭度图像,并对峭度图像进行离散余弦变换(DCT),采用较大的DCT系数构造视频指纹,在视频指纹的匹配过程中,通过滑动窗的方法对不同长度的指纹进行匹配,从而达到视频认证的目的。实验结果证明,该算法提取的视频指纹在常见视频攻击下误码率均小于10%。     

A dynamic causal topic model for mining activities from complex videos

In this paper, a novel probabilistic topic model is proposed for mining activities from complex video surveillance scenes. In order to handle the temporal nature of the video data, we devise a dynamical causal topic model (DCTM) that can detect the latent topics and causal interactions between them. The model is based on the assumption that all temporal relationships between latent topics at neighboring time steps follow a noisy-OR distribution. And the parameter of the noisy-OR distribution is estimated by a data driven approach based on the idea of nonparametric Granger causality statistic. Furthermore, for convergence analysis during model learning process, the Kullback-Leibler between the prior and the posterior distributions is calculated. At last, using the causality matrix learned by DCTM, the total causal influence of each topic is measured. We evaluate the proposed model through experimentations on several challenging datasets and demonstrate that our model can identify the high influence activity in crowded scenes.     

基于聚类的视频专题演化分析方法

视频专题演化分析有助于从海量的视频数据中发现有价值的模式。研究了基于聚类的视频专题演化分析方法,首先基于二部图对视频的视觉相似性进行分析;在此基础上,为增强同一专题视频之间的关联度以及不同专题视频之间的区分度,采用基于链路分析的方法对视频专题进行聚类,进而对视频专题的演化过程进行分析;最后通过实验证明了所提方法的有效性。     

利用概率主题模型的微博热点话题发现方法

微博具有长度短、实时传播、结构复杂以及变形词多等特点,传统的向量空间模型（VSM）文本表示方法和隐含语义分析（LSA）无法很好的对其进行建模。提出了一种基于概率潜在语义分析（pLSA）和 K 均值聚类（Kmeans）的二阶段聚类算法,此外通过定义微博热度分析和排序,有效地支持微博热点话题发现。实验表明,此方法能有效地进行话题聚类并检测出热点话题。     

基于语义人脸的视频新闻标注

视频和图像中的人脸蕴涵了丰富的语义信息，可以使用人脸对视频内容进行分析与标注，尤其是视频新闻节日。而要达到这样的目的，就必须先将对视频新闻具有语义价值的人脸从视频流中检测出来。本文提出基于语义人脸捡测的视频新闻语义聚类与标注算法：在这个算法中，首先使用肤色模型检测人脸可能出现区域，然后提取人脸可能区域的独立成分特征，用训练好的支持向量机检测出所有人脸，套用语义人脸模板过滤出最终的语义人脸集合，最后通过高斯混合聚类，将视频新闻标注为主持人镜头、访谈类新闻镜头和其他新闻故事镜头三类。实验表明，该算法在视频新闻结构化中可以得到较好的应用。     

Rank order clustering and imperialist competitive optimization based cost and RAM analysis on different industrial sectors

The competitive global scenario faces the corresponding impacts such as difficulties in scheduling and loading, high tooling and equipment investment, enormous scrap availability, complex to control the quality, and extensive setup time. So, a higher level of connectivity is required in between the design and manufacturing activities to raise the profitability of the firms and enrich the product support design. In any manufacturing firm, poor reliability causes failure availability in all stages, namely, design, construction, planning, and maintenance, etc. In this research work, RAM is analyzed from the Weibull distribution based Mean time to repair (MTTR) and mean time to failure rates (MTTF) for the ten different industries. The measured RAM performances are optimized by the Imperialist competitive algorithm (ICA) by the application of the rank order clustering (ROC) method. The failure rates are clustered and ranked by using the rank order clustering method. Besides, the cost and RAM performance values are predicted by ICA and hybrid ROC method, and such a proposed algorithm is mathematically modeled in the Mat Lab platform. Hence from the evaluation, the proposed method scores better industrial performances than the actual and other implemented methods     

Content-based copy detection by a subspace learning based video fingerprinting scheme

We propose a video copy detection scheme that employs a transform domain global video fingerprinting method. Video fingerprinting has been performed by the subspace learning based on nonnegative matrix factorization (NMF). It is shown that the binary video fingerprints extracted from the basis and gain matrices of the NMF representation enable us to efficiently represent the spatial and temporal content of a video segment respectively. An extensive performance evaluation has been carried out on the query and reference dataset of CBCD task of TRECVID 2011. Our results are compared with the average and the best performance reported for the task. Also NDCR and F1 rates are reported in comparison to the performance achieved via the global methods designed by the TRECVID 2011 participants. Results demonstrate that the proposed method achieves higher correct detection rates with good localization capability for the transformation of text/logo insertion, strong re-encoding, frame dropping, noise addition, gamma change or their mixtures; however there is still potential for improvement to detect copies with picture-in-picture transformations. It is also concluded that the introduced binary fingerprinting scheme is superior to the existing transform based methods in terms of the compactness.     

Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and the intermediate topics corresponding to human action categories. This is achieved by using latent topic models such as the probabilistic Latent Semantic Analysis (pLSA) model and Latent Dirichlet Allocation (LDA). Our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the probabilistic models. Given a novel video sequence, the algorithm can categorize and localize the human action(s) contained in the video. We test our algorithm on three challenging datasets: the KTH human motion dataset, the Weizmann human action dataset, and a recent dataset of figure skating actions. Our results reflect the promise of such a simple approach. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions.     

联合主题模型的标签聚类方法*

提升标签聚类的质量是识别标签语义的一个关键问题.文中提出基于资源的联合主题模型标签聚类方法.利用资源的引用关系,采用随机游走的方法获取资源的权威度分数,以此设置“资源-标签”和“资源-词”这2个二元关系的权重.在此基础上,构建基于资源加权的词与标签的联合潜在狄利克雷分布(LDA)模型,通过迭代学习,获取标签的潜在主题,并根据主题最大隶属度聚类标签.实验表明,相比其它基于资源的标签聚类方法,文中方法能获取更好的聚类效果.     

基于仿射包和伪Zernike矩的视频人脸检索

近年来基于视频的人脸检索已成为人脸识别和检索领域最为活跃的研究方向之一。提出了一种基于仿射包结合伪Zernike矩特征的视频人脸检索算法（FRIVAP）。在视频中检测跟踪到人脸生成图像集,接着提取图像集中人脸的伪Zernike矩特征,建立特征的仿射包,通过相似度计算得到结果。经对Honda/UCSD视频数据库和自行构建的视频数据库的大量实验表明,该算法可以充分利用视频中人脸的时间和空间信息,并且对噪声、人脸姿势变化等条件下的人脸检索有较强的鲁棒性。     

Face detection using quantized skin color regions merging andwavelet packet analysis

Detecting and recognizing human faces automatically in digital images strongly enhance content-based video indexing systems. In this paper, a novel scheme for human faces detection in color images under nonconstrained scene conditions, such as the presence of a complex background and uncontrolled illumination, is presented. Color clustering and filtering using approximations of the YCbCr and HSV skin color subspaces are applied on the original image, providing quantized skin color regions. A merging stage is then iteratively performed on the set of homogeneous skin color regions in the color quantized image, in order to provide a set of potential face areas. Constraints related to shape and size of faces are applied, and face intensity texture is analyzed by performing a wavelet packet decomposition on each face area candidate in order to detect human faces. The wavelet coefficients of the band filtered images characterize the face texture and a set of simple statistical deviations is extracted in order to form compact and meaningful feature vectors. Then, an efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the extracted feature vectors into face or nonface areas, using some prototype face area vectors, acquired in a previous training stage     

基于信息论的潜在概念获取与文本聚类

针对词、潜在概念、文本和主题之间的模糊关系,提出一种基于信息论的潜在概念获取与文本聚类方法.方法引入了潜在概念变量和主题变量。根据信息论中熵压缩编码理论,定义了一个全局目标函数,给出一种类似于确定性退火算法的求解算法,用以获得概念层次树以及在不同层次概念上的文本聚类结果,是一种双向软聚类方法.方法通过基于最短描述长度原则的概念选择方法,最终确定概念个数和对应的文本聚类结果.实验结果表明,所提出的方法优于基于词空间的文本聚类方法以及双向硬聚类方法.     

Probabilistic sequence clustering with spectral learning

In this paper, we derive two novel learning algorithms for time series clustering; namely for learning mixtures of Markov Models and mixtures of Hidden Markov Models. Mixture models are special latent variable models that require the usage of local search heuristics such as Expectation Maximization (EM) algorithm, that can only provide locally optimal solutions. In contrast, we make use of the spectral learning algorithms, recently popularized in the machine learning community. Under mild assumptions, spectral learning algorithms are able to estimate the parameters in latent variable models by solving systems of equations via eigendecompositions of matrices or tensors of observable moments. As such, spectral methods can be viewed as an instance of the method of moments for parameter estimation, an alternative to maximum likelihood. The popularity stems from the fact that these methods provide a computationally cheap and local optima free alternative to EM. We conduct classification experiments on human action sequences extracted from videos, clustering experiments on motion capture data and network traffic data to illustrate the viability of our approach. We conclude that the spectral methods are a practical and useful alternative in terms of computational effort and solution quality to standard iterative techniques such as EM in several sequence clustering applications.     

基于跨语言广义向量空间模型的跨语言文档聚类方法

跨语言文档聚类主要是将跨语言文档按照内容或者话题组织为不同的类簇。该文通过采用跨语言词相似度计算将单语广义向量空间模型(Generalized Vector Space Model, GVSM)拓展到跨语言文档表示中,即跨语言广义空间向量模型(Cross-Lingual Generalized Vector Space Model,CLGVSM),并且比较了不同相似度在文档聚类下的性能。同时提出了适用于GVSM的特征选择算法。实验证明,采用SOCPMI词汇相似度度量算法构造GVSM时,跨语言文档聚类的性能优于LSA。     

Exploiting scene maps and spatial relationships in quasi-static scenes for video face clustering

Video face clustering is a fundamental step in automatically annotating a video in terms of when and where (i.e., in which video shot and where in a video frame) a given person is visible. State-of-the-art face clustering solutions typically rely on the information derived from visual appearances of the face images. This is challenging because of a high degree of variation in these visual appearances due to factors like scale, viewpoint, head pose and facial expression. As a result, either the generated face clusters are not sufficiently pure, or their number is much higher than that of people appearing in the video. A possible way towards improved clustering performance is to analyze visual appearances of faces in specific contexts and take the contextual information into account when designing the clustering algorithm. In this paper, we focus on the context of quasi-static scenes, in which we can assume that the people's positions in a scene are (quasi-)stationary. We present a novel video clustering algorithm that exploits this property to match faces and efficiently propagate face labels across the scope of viewpoints, scale and level of zoom characterizing different frames and shots of a video. We also present a novel publicly available dataset of manually annotated quasi-static scene videos. Experimental assessment on the latter indicates that exploiting information derived by the scene and the spatial relationships between people can substantially improve the clustering performance compared to the state-of-the-art in the field.     

基于潜在语义索引和句子聚类的中文自动文摘

自动文摘是自然语言处理领域的一项重要的研究课题.提出一种基于潜在语义索引和句子聚类的中文自动文摘方法.该方法的特色在于：使用潜在语义索引计算句子的相似度,并将层次聚类算法和K-中心聚类算法相结合进行句子聚类,这样提高了句子相似度计算和主题划分的准确性,有利于生成的文摘在全面覆盖文档主题的同时减少自身的冗余.实验结果验证了该文提出的方法的有效性,对比传统的基于聚类的自动文摘方法,该方法生成的文摘质量获得了显著的提高.     

基于BTM和K-means的微博话题检测

近年来,微博等社交网络的发展给人们的沟通交流提供了方便。由于每条微博都限定在140字以内,因此产生了大量的短文本信息。从短文本中发现话题日渐成为一项重要的课题。传统的话题模型(如概率潜在语义分析(PLSA)、潜在狄利克雷分配(LDA)等) 在处理短文本方面都面临着严重的数据稀疏问题。另外,当数据集比较集中并且话题文档间的差别较明显时,K-means 聚类算法能够聚类出有区分度的话题。引入BTM话题模型来处理微博数据这样的短文本,以缓解数据稀疏的问题。同时,整合了K-means聚类算法来对BTM模型所发现的话题进行聚类。在新浪微博短文本集上进行的实验证明了此方法发现话题的有效性。