A Two-Stage Feature Selection Method for Text Categorization by Using Category Correlation Degree and Latent Semantic Indexing

With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(CCD)method and latent semantic indexing(LSI).In the first stage,a novel CCD method is proposed to select the most effective features for text classification,which is more effective than the traditional feature selection method.In the second stage,document representation requires a high dimensionality of the feature space and does not take into account the semantic relation between features,which leads to a poor categorization accuracy.So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the individual terms which can discover the important correlative relationship between features and reduce the feature space dimension.Firstly,each feature in our algorithm is ranked depending on their importance of classification using CCD method.Secondly,we construct a new semantic space based on LSI method among features.The experimental results have proved that our method can reduce effectively the dimension of text vector and improve the performance of text categorization.     

一种基于核局部保留投影的人脸识别方法

为了有效提取人脸的非线性结构信息,提出一种新的基于最大散度差的核判别局部保留投影方法.首先通过核函数将样本数据映射到高维特征空间,计算特征空间中样本的散度矩阵,其次将样本原始空间中的近邻图嵌入到散度矩阵,最后采用最大散度差准则进行特征提取.在PIE与Yale人脸数据库上的实验结果表明,提出的人脸识别方法最高识别率可达到99%.      

多尺度注意力学习的Faster R-CNN口罩人脸检测模型

针对在佩戴口罩等有遮挡条件下的人脸检测问题，提出了多尺度注意力学习的Faster R-CNN （MSAF R-CNN）人脸检测模型. 首先，为充分考虑人脸目标多尺度信息，相较于原始Faster R-CNN框架，引入Res2Net分组残差结构，获取更细粒度的特征表征；其次，基于空间-通道注意力结构改进的Res2Net模块，结合注意力机制自适应学习目标不同尺度特征；最后，为学习目标的全局信息并减轻过拟合现象，在模型顶端嵌入加权空间金字塔池化网络，采用由粗到细的方式进行特征尺度划分. 在AIZOO和FMDD两个人脸数据集上的实验结果表明:所提出MSAF R-CNN模型对佩戴口罩的人脸检测准确率分别达到90.37%和90.11%，验证了模型的可行性和有效性.      

Mobile Robot Hierarchical Simultaneous Localization and Mapping Using Monocular Vision

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF), where the particle filter is used to extending the path posterior by sampling new poses. The landmark position estimation and update is implemented through extended Kalman filter (EKF). Monocular vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching scale invariant feature transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree in the time cost of O(lbN). Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.     

一种结合2DLPP与2DPCA的人脸识别方法

为解决二维局部保持投影(2DLPP)需要较多数据表示人脸特征的缺陷,提出了一种新的二维局部保持投影主成分分析方法(2DLPP-PCA).通过对人脸图像在行、列方向同时进行2DLPP和2DPCA投影,2DLPP-PCA 不仅能减少保存人脸特征所需要的数据量,而且能有效地提取人脸的局部特征和全局特征.在ORL、Yale和CAS-PEAL-R1人脸数据库上的实验结果表明,2DLPP-PCA是一种高性能的特征提取方法,当训练样本数为6时,2DLPP-PCA在ORL数据库上的最佳平均识别率达到99％以上.     

A Novel Gyrocompass Alignment Method Under Large Azimuth Misalignment Angle

Conventional gyrocompass alignment methods are based on relatively small azimuth misalignment angles.However,a marine strapdown inertial navigation system may face large azimuth misalignment angle caused by a failed coarse alignment algorithm.This paper provides a novel gyrocompass alignment method to solve the problem.Effects of system parameters are analyzed and the proper scenario of parameter switch based on the classic control theories is derived.Test results show that compared with the conventional methods,our method can accomplish the initial alignment quickly and accurately under large azimuth misalignment angle.     

用于检测疲劳驾驶的自适应脸部定位方法

针对疲劳驾驶视觉检测技术中驾驶员脸部定位快速性与准确性难以同时满足的情况,提出了一种将肤色和面部几何特征相结合的自适应脸部定位方法。该方法对YCbCr空间肤色相似度和传统Otsu闾值分割算法进行改进,降低了计算复杂度,采用新的区域连通和区域标记算法定位出脸部位置。最后,根据人脸几何特征对脸部区域做进一步确认：试验结果表明,该方法能对驾驶员图像脸部进行快速、准确定位。     

车辆传动系统变参小波流形融合故障诊断方法

应用流形学习方法非线性融合信号在不同小波参数下中央尺度对应的小波包络,研究了强背景噪声下车辆传动系统振动信号故障瞬态脉冲包络的有效提取问题,并与传统信号时频分解方法进行了对比研究;采用不同小波参数对振动信号进行连续小波变换,提取了每组参数下中央尺度上的小波包络;采用基尼指数选择若干包含故障瞬态脉冲信息的小波包络,构造了高维小波包络矩阵;采用局部切空间排列算法对高维小波包络进行流形融合,获得了反映故障瞬态脉冲包络本质结构的小波包络流形;为了验证所提方法的有效性和优越性,采用不同方法对轨道车辆轮对轴承和汽车变速齿轮箱的故障振动信号进行了对比分析。研究结果表明:在分析轴承外圈故障信号时,所提方法基尼指数比传统信号时频分解方法提高27.32%以上;在分析齿轮磨损故障信号时,所提方法基尼指数比传统信号时频分解方法提高26.74%以上。可见,所提方法通过综合具有不同形态的变参小波包络,可以在无需优化小波参数情况下,对车辆传动系统中的不同关键部件故障振动信号具有较好的自适应性,提取的故障脉冲包络中的带内噪声少,故障脉冲特性明显,容易识别其频谱中的故障特征频率,是检测车辆传动系统故障的一种有效方法。      

基于联合投票网络的交通场景天气分类方法

基于交通视频监控图像的天气识别已经成为智能交通系统中重要的研究课题.虽然卷积神经网络(convolutional neural network,CNN)在图像识别技术获得了巨大的发展,但是针对复杂交通场景的天气识别问题,现有的模型在特征表达方面仍然面临着巨大的挑战.为了提取丰富的语义特征,提出了基于联合投票机制的深度神...     

Range profile target recognition using sparse representation based on feature space

A novel method is presented to improve the recognition rate of warhead in this paper. Firstly, a tool for electromagnetic calculation, like CST Microwave Studio, is used to simulate the frequency response of the electromagnetic scattering. Secondly, the echo and further the range profile are acquired from the frequency response by further processing. Thirdly, a set of discriminative features is extracted from the range profiles of the target. Fourthly, these features are used to construct a dictionary for the sparse representation classifier. Finally, the sample of the target can be classified by solving the sparsest coefficients. Since the reconstruction result is determined by a linear combination of the training samples, this method has a good robustness for the variable features. By formulating the problem within a feature-based sparse representation framework, the presented method combines the discriminative features of each sample during the sparse recovery process rather than in a postprocessing manner. Moreover, based on the feature representation space rather than a single feature or image pixel, the constructed dictionary exhibits both strong expressive and discriminative powers that can enhance the classification performance of the test sample. A series of test results based on the simulated data demonstrates the effectiveness of our method.     

驾驶人手机通话行为中基于图像特征决策融合的手势识别方法

为鲁棒检测自然环境中驾驶人的通话行为, 提出了一种驾驶人手机通话手势的识别方法。运用Adaboost算法检测驾驶人面部区域, 在YC_gC_r色彩空间中分别对面部肤色亮度分量和色度分量进行稀疏网格间隔采样, 由此建立了肤色的高斯分布模型; 针对驾驶室光照强度的不均匀性, 提出了肤色分量的漂移补偿算法, 建立了适应光照变化的在线肤色模型, 以准确分割左右手部肤色区域; 运用HOG算法获取手部肤色区域的2 376维HOG特征向量, 运用PCA方法将HOG特征降至400维; 同时提取手部肤色区域的PZMs特征, 并采用Relief算法筛选出权重最大的8个PZMs特征向量, 建立了融合PCA-HOG特征和Relief-PZMs特征的通话手势支持向量机分类决策。试验结果表明: 基于PCA-HOG特征的手势识别率为93.1%, 对光照变化的鲁棒性较好, 但易受到手部与头部转动的干扰; 基于Relief-PZMs特征的手势识别率为91.9%, 对于头部与手部姿态的耐受度较好, 但光照鲁棒性较差; 基于PCA-HOG和Relief-PZMs多元特征融合方法的手势识别率达到94.5%, 对光照波动、手部与头部转动等干扰条件具有较好的适应性。     

基于自适应尺度LE的轮廓编组算法

在图嵌入框架下，以编组线索的聚类来实现轮廓编组的方法，不仅可以有效地将局部特征和全局特征结合起来，而且更加符合人类视觉感知以流形存在的特点．本文在分析相似度矩阵对样本结构表示意义的基础上，提出了一种基于自适应尺度LaplacianEigenmap的轮廓编组算法．该算法能够根据编组线索多维特征的不同局部统计特性，自适应地改变相似度计算模型中的尺度因子，使相似度矩阵更准确地反映编组线索数据集的结构特性．在此基础上通过降维实现编组元的聚类，从而得到轮廓编组的结果．实验证明，该算法对于局部统计特性差异较大的编组线索有着良好的适应性，尤其对于有遮挡的感知目标，表现出比图分割方法更为优越的性能．     

Incremental Supervised Subspace Learning for Face Recognition

Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.     

邮件过滤系统中预处理方法的研究与实现

完成了邮件过滤系统中的预处理工作。实现了信息增益特征选择算法,通过实验对比,得出了PU系列语料库合适的特征维数。使用词频反文档频率公式计算了特征词的权重,通过算法把标准邮件集处理成了支持向量机算法可以直接处理的向量空间模型的形式。     

基于RBM-GASA-BPNN 的潜在高价值旅客预测

针对用BP神经网络(Back Propagation Neural Network, BPNN)进行潜在高价值旅客预测时出现的特征表达能力弱、稳定性差、易陷入局部极值的不足,提出一种新颖的基于 RBM-GASA-BPNN的潜在高价值旅客预测方法.该方法首先通过聚类算法划分旅客类别,设置类别标签;然后利用受限玻尔兹曼机(Restricted Boltzmann Machine, RBM)提取旅客行为特征并确定最优BPNN 初始权值和偏置的寻优范围,又利用遗传模拟退火算法(Genetic Algorithm-Simulate Anneal, GASA)对BPNN参数进行精调,确定了最优的BPNN初始权值和偏置;最后,利用优化后的BPNN对旅客进行分类预测.实验结果表明,本文提出的方法克服了基于BPNN的分类预测方法的缺陷,具有更高的分类预测准确率和潜在高价值旅客预测能力.     

基于RBM-GASA-BPNN的潜在 高价值旅客预测

针对用BP神经网络(Back Propagation Neural Network, BPNN)进行潜在高价值旅客预测时出现的特征表达能力弱、稳定性差、易陷入局部极值的不足,提出一种新颖的基于 RBM-GASA-BPNN的潜在高价值旅客预测方法.该方法首先通过聚类算法划分旅客类别,设置类别标签;然后利用受限玻尔兹曼机(Restricted Boltzmann Machine, RBM)提取旅客行为特征并确定最优BPNN 初始权值和偏置的寻优范围,又利用遗传模拟退火算法(Genetic Algorithm-Simulate Anneal, GASA)对BPNN参数进行精调,确定了最优的BPNN初始权值和偏置;最后,利用优化后的BPNN对旅客进行分类预测.实验结果表明,本文提出的方法克服了基于BPNN的分类预测方法的缺陷,具有更高的分类预测准确率和潜在高价值旅客预测能力.     

Latent Supportive Utility of Irrelevant Attributes in Feature Selection

This paper proposed a novel feature selection method LUIFS (latent utility of irrelevant feature selection) that not only selects the relevant features, but also targets at discovering the latent useful irrelevant attributes by measuring their supportive importance to other attributes. The method minimizes the information lost and simultaneously maximizes the final classification accuracy. The classification error rates of the LUIFS method on 16 real-life datasets from UCI machine learning repository were evaluated using the ID3, Nave-Bayes, and IB (instance-based classifier) learning algorithms, respectively; and compared with those of the same algorithms with no feature selection (NoFS), feature subset selection (FSS), and correlation-based feature selection (CFS). The empirical results demonstrate that the LUIFS can improve the performance of learning algorithms by taking the latent relevance for irrelevant attributes into consideration, and hence including those potentially important attributes into the optimal feature subset for classification.     

基于融合两类核Fisher鉴别函数的人脸识别方法

提出了一种基于两类核Fisher鉴别分析(KFDA)的人脸识别方法,对每2个不同人脸类别求解一个核Fisher鉴别函数,其优点是能针对特定的2个人脸图像类别,抽取区分该2类人脸的最佳鉴别特征,克服了多类KFDA和2类KFDA相比是次优的问题.为解决KFDA计算量大的问题,将MSE推广为基于核的MSE(KMSE),用其得到核Fisher鉴别函数,减少了训练和识别的计算时间.在识别阶段应用了两种融合方法融合各个基于KMSE的核Fisher鉴别函数.     

融合式空间塔式算子和HIK-SVM的交通标志识别研究

在交通标志识别问题上,提出了一种基于融合式的空间塔式算子和直方图交叉核支持向量机(HIK-SVM)的分类方法.在该方法中,通过提取图像的灰度塔式词袋直方图(Gray-PHOW)特征、颜色塔式词袋直方图(Color-PHOW)特征和塔式边缘方向梯度直方图(PHOG)特征来对交通标志的外观、颜色和轮廓信息进行描述.通过提取空间塔式直方图特征,能很好地对图像各种特征的空间分布状况进行描述.提取到图像的外观、颜色、轮廓和特征的空间分布信息后,对其进行融合,最后得到的融合式的空间塔式特征具有很强的鲁棒性.将该融合式特征送入HIK-SVM进行训练和分类,取得了极其高的识别效果.     

基于深度学习的网约车供需缺口短时预测研究

城市不同区域网约车供需缺口预测可为车辆调度策略提供支持,从而提高车辆运行效率和乘客服务水平.为实现网约车供需缺口短时预测,提出一种基于时空数据挖掘的深度学习预测模型(Spatio-Temporal Deep Learning Model, S-TDL).该模型由时空变量模型、空间属性变量模型和环境变量模型 3个子模型融合而成,可捕捉时空关联性、区域差异性和环境变化对供需缺口的影响.同时,提出特征聚类-最大信息系数两阶段特征选择方法,筛选与供需缺口相关性强的特征变量,提高训练效率,减少过拟合.滴滴出行实例分析证明,特征选择后的 STDL模型预测精度显著优于BP神经网络、长短期记忆网络和卷积神经网络.