复杂场景下结合SIFT与核稀疏表示的交通目标分类识别

针对复杂场景下的交通目标分类识别难点,提出一种基于尺度不变特征转换(SIFT)与核稀疏表示的分类识别算法.该算法首先利用SIFT分别提取训练样本和待测目标局部特征信息,通过核方法将特征样本映射到核空间,构建过完备字典,最后通过待测目标在字典中的稀疏度与重构误差对交通目标类别进行判定.同时,分析了随机投影下的核稀疏表示分类与特征维数之间的关系.实验结果表明,与SVM、稀疏表示分类(SRC)相比,该方法增强了交通目标特征层的类判别能力,具有较好的识别率和鲁棒性.     

核不相关辨别子空间雷达目标一维像识别

针对不相关辨别分析方法在目标类别数较多时计算量大,且可能面临散度矩阵奇异的问题,提出了一种核不相关辨别子空间算法,并将其用于雷达目标一维距离像识别。新算法继承了原方法提取目标统计不相关辨别特征的优点,同时利用核机器学习理论与广义奇异值分解,有效解决了计算量与矩阵奇异的问题,并进一步改善了目标的类可分性。对ISAR实测飞机数据进行了分类,并与几种经典核非线性方法进行了比较,结果表明所提方法的识别性能得到了明显改善。     

基于核Fisher判别字典学习的稀疏表示分类

在基于稀疏表示分类的模式识别中,字典学习(DL) 可以为稀疏表示获得更为精简的数据表示。最近的基于Fisher判别的字典学习(FDDL)可以学 习到更加判别的稀疏字典,使得稀疏表示分类具有很强的识别性能。核空间变换可以学习到 非线性结构信息,这对判别分类非常有用。为了充分利用 核空间特性以学习更加判别的稀疏字典来提升最终的识别性能,在FDDL的基础上,提出了两 种核化的稀疏表示DL方法。首先原始训练数据被投影到高维核空间,进行基于Fisher 判别的核稀 疏表示DLFDKDL;其次在稀疏系数上附加核Fisher约束,进行基于核Fisher判别的核稀疏表 示DL(KFDKDL),使得所学习的字典具有更强的判别能力。在多个公开的图像数据库上的稀疏 表示分类实验结果验证了所提出的FDKDL和KFDKDL方法的有效性。     

基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别

针对多基地水下小目标分类识别问题,本文提出了一种基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别方法 .对水下目标多角度散射信号提取6种典型的具有信息互补性和关联性的特征,提出一种随机森林（Random Forest,RF）和最小冗余最大相关（minimum Redundancy and Maximum Relevance,mRMR）相结合的特征选择方法（RF-mRMR）,得出综合的特征重要性排序结果 .通过实验得出分类模型所需的最优特征子集,达到降低数据处理复杂度和提高目标分类结果的目的 .为了捕捉到数据中的高阶结构,在联合稀疏表示模型的基础上,使用核函数将线性不可分的特征数据映射到高维核特征空间.为了充分挖掘稀疏重构后包含在残差波段中的有用信息,使用指数平滑公式对具有一定意义的残差信息进行再利用,最后由核特征空间下的最小误差准则判定目标的类别.应用本文提出的方法对4类目标的海试数据进行识别,结果表明,相较于其他7种对比算法,本文提出的改进方法具有更好的分类性能,而且大多数情况下,本文提出的算法在双基地声呐模式下具有比单基地声呐更高的识别准确率和更低的虚警率.     

基于滑动平均和高阶统计量的数字调制识别

提出一种滑动平均和高阶统计量结合的优化识别方法。先对接收信号平滑优化，再利用高阶统计量区分信号，使得信号在低信噪比且未知信号和噪声功率的情况下也能有较高的识别率。该方法计算简单，有效地抑制了噪声影响，改善了原算法的识别效果。论文进行了理论推导，并用仿真实验进行了验证。     

基于自学习稀疏表示的动态手势识别方法

针对加速度传感器的手势采集方式提出一种基于自学习稀疏表示的动态手势识别方法。该方法将分类识别问题转化为求解待识别样本对于训练样本的稀疏表示问题,直接对原始加速度信号进行操作,省去了特征提取过程,可方便地添加新的手势类别和删除已有的手势类别;利用面向类别的字典学习,来寻求一个较小的并经过优化的超完备字典来计算待识别样本的稀疏表示,从而大大缩减算法的计算复杂度,满足实时性要求。在包含18种手势的3 000多个样本的公开数据集上进行测试,实验结果验证了该方法的有效性。     

基于聚类中心GDA的一维像目标识别方法

对于目标高分辨一维距离像的目标识别,给出了一种基于广义判别分析(GDA)的特征非线性映射方法,将所提取的功率谱特征参数优化后,使目标特征参数在非线性特征空间中的Fisher比增大,使用基于高斯混合模型的分类器进行目标识别.针对训练样本多时GDA计算代价大,特征提取速度慢问题,提出了一种将训练样本做聚类后使用聚类中心作为训练样本计算GDA模型参数的快速算法,使模型训练时间及其特征识别时间均有很大程度上的减少,并且识别效果也相对与抽样选择训练样本有明显提高,实验结果表明了文中所提方法的有效性.     

高阶累积量和分形理论在信号调制识别中的应用研究

提出了将信号高阶累积量和分形盒维数相结合的特征提取方法。信号高阶累积量特征具有良好的抗噪性能,被广泛应用于调制识别。2ASK和BPSK的高阶累积量、以及2FSK,4FSK,8FSK的高阶累积量相等,使得只提取信号高阶累积量不足以区分信号。针对这一问题,引入信号的分形盒维数,提取信号的高阶累积量和分形盒维数构成联合特征参数,构建级联神经网络分类器,对信号进一步进行分类。对2ASK, 4ASK, BPSK, 4PSK, 2FSK, 4FSK, 16QAM七种信号进行了仿真,结果表明,该方法提取的特征参数计算复杂度低,具有较好的抗噪性能。在信噪比不低于5dB、测试样本数不少于200的条件下,正确识别率达到了85%以上。      

基于KLLE和KNR的雷达目标一维像识别

局部线性嵌入(LLE)是一种有代表性的流形学习算法,利用核技术将LLE进行推广,得到核局部线性嵌入算法,并将其应用于雷达目标一维距离像的特征提取。然后采用一种基于核的非线性分类器,对所提取的特征进行分类。对3种飞机的实测数据进行识别实验,结果表明,该方法具有较优的识别性能。     

基于模糊核Fisher判别的雷达距离像识别研究

核Fisher判别分析(KFDA)既具有核技巧的非线性描述能力,又继承了线性Fisher判别分析(FDA)的优点,其在非线性判别方面表现出很好的识别性能。尝试把训练核样本的模糊隶属度信息完全融入到核散布矩阵的计算中,使得KFDA在提取原始训练样本非线性特征的同时,又可以提取到训练核样本的隶属度信息,并且把这种方法应用到雷达一维距离像识别中,实验证明其取得了很好的识别效果。     

Object classification in 3-D images using alpha-trimmed mean radialbasis function network

We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training. A new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images.     

Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels

Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their high accuracies. However, it is difficult to visualize the classifiers, and thus difficult to provide intuitive interpretation of results to physicians. We developed a new nonlinear kernel, the localized radial basis function (LRBF) kernel, and new visualization system visualization for risk factor analysis (VRIFA) that applies a nomogram and LRBF kernel to visualize the results of nonlinear SVMs and improve the interpretability of results while maintaining high prediction accuracy. Three representative medical datasets from the University of California, Irvine repository and Statlog dataset-breast cancer, diabetes, and heart disease datasets-were used to evaluate the system. The results showed that the classification performance of the LRBF is comparable with that of the RBF, and the LRBF is easy to visualize via a nomogram. Our study also showed that the LRBF kernel is less sensitive to noise features than the RBF kernel, whereas the LRBF kernel degrades the prediction accuracy more when important features are eliminated. We demonstrated the VRIFA system, which visualizes the results of linear and nonlinear SVMs with LRBF kernels, on the three datasets.     

卷积神经网络（CNN）训练中卷积核初始化方法研究

本文提出了一种基于样本图像局部模式聚类的卷积核初始化方法,该方法可用于卷积神经网络（Convolutional neural network, CNN）训练中卷积核的初始化。在卷积神经网络中,卷积核的主要作用可看成是利用匹配滤波提取图像中的局部模式,并将其作为后续图像目标识别的特征。为此本文在图像训练集中选取一部分典型的样本图像,在这些图像中抽取与卷积核相同大小的子图作为图像局部模式矢量集合。首先对局部模式子图集合应用拓扑特性进行粗分类,然后对粗分类后的每一子类采用势函数聚类的方法获取样本图像中的典型局部模式子图,构成候选子图模式集,用它们作为CNN的初始卷积核进行训练。实验结果表明,本文方法可以明显加速CNN网络训练初期的收敛速度,同时对最终训练后的网络识别精度也有一定程度的提高。      

Adaptive Bayesian contextual classification based on Markov random fields

An adaptive Bayesian contextual classification procedure that utilizes both spectral and spatial interpixel dependency contexts in estimation of statistics and classification is proposed. Essentially, this classifier is the constructive coupling of an adaptive classification procedure and a Bayesian contextual classification procedure. In this classifier, the joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Markov random field. The estimation of statistics and classification are performed in a recursive manner to allow the establishment of the positive-feedback process in a computationally efficient manner. Experiments with real hyperspectral data show that, starting with a small training sample set, this classifier can reach classification accuracies similar to that obtained by a pixelwise maximum likelihood pixel classifier with a very large training sample set. Additionally, classification maps are produced that have significantly less speckle error.     

一种创新性激光图像三维目标识别算法

针对径向基神经网络在激光图像分类识别中识别率低及训练时间长的问题,提出粗糙集与神经网络相结合的方法,将粗糙集算法简约后的样本特征作为神经网络的前置输入。首先建立不同视点的激光主动成像三维仿真图像,然后提取17个目标特征,并采用粗糙集算法选择分类的属性,从17个特征中筛选出5个影响决策的特征属性,最后选用4层径向基神经网络作为基本的网络结构,并采用在各层节点上与粗糙集相结合方法识别目标。仿真结果表明,结合粗糙集的集成神经网络方法识别正确率保持在80%以上,与未结合粗糙集的神经网络相当,但训练与识别时间缩短10倍以上。     

ECG analysis using nonlinear PCA neural networks for ischemiadetection

The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats     

凸组合核函数的支持向量机高光谱图像分类

支持向量机的高光谱图像分类中,单核函数存在局限性。为了提高分类器的分类精度和支持向量机模型的泛化能力,利用高斯径向基核和多层感知核进行凸组合构造复合核函数支持向量机,证明了该函数满足作为核函数的判决Mercer条件,并进一步将凸组合核函数支持向量机应用到高光谱图像分类中,完成了建模和实验验证。实验结果表明,凸组合核函数具有较好的鲁棒性,且该类支持向量机的分类精度和KAPPA系数较单核SVM均得到了有效的提高,是一种解决多分类问题行之有效的分类器。     

Blind channel identification and equalization withmodulation-induced cyclostationarity

Recent results have pointed out the importance of inducing cyclostationarity at the transmitter for blind identification and equalization of communication channels. This paper addresses blind channel identification and equalization relying on the modulation-induced cyclostationarity, without introducing redundancy at the transmitter. It is shown that single-input single-output channels can be identified uniquely from output second-order cyclic statistics, irrespective of the location of channel zeros, color of additive stationary noise, or channel order overestimation errors, provided that the period of modulation-induced cyclostationarity is greater than half the channel length. Linear, closed-form, nonlinear correlation matching, and subspace-based approaches are developed for channel estimation and are tested using simulations. Necessary and sufficient blind channel identifiability conditions are presented. A Wiener cyclic equalizer is also proposed     

Radar HRRP target recognition based on higher order spectra

Radar high-resolution range profile (HRRP) is very sensitive to time-shift and target-aspect variation; therefore, HRRP-based radar automatic target recognition (RATR) requires efficient time-shift invariant features and robust feature templates. Although higher order spectra are a set of well-known time-shift invariant features, direct use of them (except for power spectrum) is impractical due to their complexity. A method for calculating the Euclidean distance in higher order spectra feature space is proposed in this paper, which avoids calculating the higher order spectra, effectively reducing the computation complexity and storage requirement. Moreover, according to the widely used scattering center model, theoretical analysis and experimental results in this paper show that the feature vector extracted from the average profile in a small target-aspect sector has better generalization performance than the average feature vector in the same sector when both of them are used as feature templates in HRRP-based RATR. The proposed Euclidean distance calculation method and average profile-based template database are applied to two classification algorithms [the template matching method (TMM) and the radial basis function network (RBFN)] to evaluate the recognition performances of higher order spectra features. Experimental results for measured data show that the power spectrum has the best recognition performance among higher order spectra.     

基于非线性特征提取和SVM的人脸识别算法

传统的PCA或LDA都是从像素的二阶依赖上考虑的,对于多像素之间的依赖性或像素的高阶关系不敏感。该文利用核函数方法提取像素高阶相关,并与线性SVM相结合来进行人脸识别。从Yale人脸库上的实验结果可以看出,非线性特征提取是很有效的,并且SVM分类器的性能优于最近邻分类器。