低层次和高层次特征相结合的人体动作识别

为了准确提取人体动作特征,提出了一种新的基于二维Gabor滤波器的时空兴趣点检测器,该检测器对遮挡,光照变化以及镜头缩放等具有较强的鲁棒性。基于80面体模型在一定大小的时空邻域内提取精细的时空梯度信息进一步刻画人体动作在时空上的视觉特征。采用最大似然估计得到对每段动作视频的权重直方图估计,使算法更有效率且权重直方图描述特征更具区分度。将低层次的权重直方图特征和高层次的动作语义属性融合,采用隐支持向量机求解最终动作识别模型的局部最优解。在几种典型的数据库上对算法进行了验证,与现有方法相比较,识别率有了较大的提高。     

Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization

The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unaligned biopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovations expand the range of problems which can be solved using EM and increase the chance of finding good solutions. First, subsequences which actually occur in the biopolymer sequences are used as starting points for the EM algorithm to increase the probability of finding globally optimal motifs. Second, the assumption that each sequence contains exactly one occurrence of the shared motif is removed. This allows multiple appearances of a motif to occur in any sequence and permits the algorithm to ignore sequences with no appearance of the shared motif, increasing its resistance to noisy data. Third, a method for probabilistically erasing shared motifs after they are found is incorporated so that several distinct motifs can be found in the same set of sequences, both when different motifs appear in different sequences and when a single sequence may contain multiple motifs. Experiments show that MEME can discover both the CRP and LexA binding sites from a set of sequences which contain one or both sites, and that MEME can discover both the –10 and –35 promoter regions in a set of E. coli sequences.     

基于轨迹词包模型的老人跌倒检测

针对异常行为检测受到光照变化、目标遮挡和计算复杂度高等因素的影响而导致检测效果不理想的问题,本文提出一种基于时空兴趣点和轨迹词包模型的异常行为检测算法。首先,利用时空兴趣提取目标的特征点信息;其次,利用稀疏光流法对特征点进行跟踪,获取目标的运动轨迹。然后,利用Meanshift聚类算法对轨迹进行聚类并构建轨迹词包模型。最后,利用SVM完成异常行为的判别。算法在不同视频数据库上进行了验证,并取得了93.3%的准确率。通过与以往的实验结果的比较,算法在异常行为检测方面具有较好的实时性、准确性和可靠性。     

用改进的非监督学习方法加速前馈神经网络的训练

本语文对非学习方法和前馈神经网络的结构进行了一些简单的改进,用改进后的结构和学习方法寝化前馈神经网络的权值,使前馈神经网络的训练速度和逼近精度显著提高。最后本语文通过仿真实例验证了该方法。     

基于无指导学习策略的无词表条件下的汉语自动分词

探讨了基于无指导学习策略和无词表条件下的汉语自动分词方法,以期对研制开放环境下健壮的分词系统有所裨益,全部分词知识源自从生语料库中自动获得的汉字Bigram．在字间互信息和t-测试差的基础上,提出了一种将两者线性叠加的新的统计量md,并引入了峰和谷的概念,进而设计了相应的分词算法,大规模开放测试结果显示,该算法关于字间位置的分词正确率为85．88％,较单独使用互信息或t-测试差分别提高了2．47％和5．66％。     

基于方向梯度的相关图算法在人体动作识别中的应用

针对人体动作的特征提取法,该文提出了基于方向梯度的相关图算法Correlogram of Oriented Gradient(COG),它是通过检查视频动作中的时间空间兴趣点和以兴趣点为中心的空间立方体,计算并提取空间立方体的时间空间方向梯度所具有的空间结构分布特性和外观信息,建立不同动作的特征模型,并用支持向量机(SVM)分类器来检验特征模型的识别准确率;最后,通过对比基于方向梯度的柱状图算法Histogram of Oriented Gradient(HOG),该文提出的COG算法的识别准确率比HOG算法高15%左右。     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

A bag of words approach to subject specific 3D human pose interaction classification with random decision forests

In this work, we investigate whether it is possible to distinguish conversational interactions from observing human motion alone, in particular subject specific gestures in 3D. We adopt Kinect sensors to obtain 3D displacement and velocity measurements, followed by wavelet decomposition to extract low level temporal features. These features are then generalized to form a visual vocabulary that can be further generalized to a set of topics from temporal distributions of visual vocabulary. A subject specific supervised learning approach based on Random Forests is used to classify the testing sequences to seven different conversational scenarios. These conversational scenarios concerned in this work have rather subtle differences among them. Unlike typical action or event recognition, each interaction in our case contain many instances of primitive motions and actions, many of which are shared among different conversation scenarios. That is the interactions we are concerned with are not micro or instant events, such as hugging and high-five, but rather interactions over a period of time that consists rather similar individual motions, micro actions and interactions. We believe this is among one of the first work that is devoted to subject specific conversational interaction classification using 3D pose features and to show this task is indeed possible.     

基于机器学习的韩国语新造词透明度探究

机器学习在诸多学科领域的定量分析中都已经显现出了巨大价值.本文借助sklearn机器学习库,以韩国国立国语院2015年发布的《新词调查报告书》中收录的新造词为对象,根据报告中出现的分类标准为词汇建立特征矩阵.而后运用多种机器学习算法进行特征选择,最终筛选出对韩国语新造词词义理解影响较强的因素.实验结果表明:如果该词为派...     

一种提高文本分类效果的改进流形学习算法

流形学习算法在模式识别领域有着重要应用,针对文本分类数据的特点,提出一种基于邻域选取进行修正的局部线性嵌入算法,用带有权值的欧式距离来构造文本数据的局部邻域,提高文本分类的识别率;同时,利用文本数据的类别信息,运用半监督局部线性嵌入算法构造分类器,提高文本分类的效果。实验表明,本文基于文本分类改进的流形学习算法,能够有效地对文本进行分类。     

Learning mixtures of point distribution models with the EM algorithm

This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct the PDM, the method iteratively refines the eigenvectors of the covariance matrix using a gradient ascent technique. Second, we show how recognition by alignment can be realised by fitting a mixture of linear shape deformations. We evaluate the method on the problem of learning the class-structure and recognising Arabic characters.     

Automatic classification of Human Epithelial type 2 cell Indirect Immunofluorescence images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (e.g., speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is composed of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.     

一种字符组合词包模型的少约束车牌定位算法

针对如何在复杂背景、拍摄角度和车牌尺度发生变化等少约束条件下实现基于字符组合词包模型的车牌定位算法.本文首先构造包含车牌数字字符和英文字符的数据库;然后利用本算法识别提取车牌字符的SIFT特征,并精准计算识别特征点在字符识别区域的相对位置、物理方向等信息组成视觉识别词汇;最后把本车牌字符的视觉词汇聚合后搭建车牌字符视觉...     

Construction and Methods of Learning of Bayesian Networks

Methods of learning Bayesian networks from databases, basic concepts of Bayesian networks, basic methods of learning, methods of learning parameters, and the structures of a network and hidden parameters are considered. Basic definitions and key concepts with illustrative examples are presented. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 133–147, July–August 2005.     

灵活运用“电大在线”,优化网络学习资源发布方式

通过实例,给出了“电大在线”下的优化网络学习资源发布方式.实践证明,该方法对激发学生的学习积极性起到了促进作用.