New learning subspace method for image feature extraction

Image pattern recognition is the most i mportant re-search directions currently,and feature extraction is itscoretechnology.Researchinthisfield dates backat leastto the 1940s ,but most feature extraction techniques re-quire users to give a priori probabil…     

基于核的最近邻特征重心分类器及人脸识别应用

本文提出核最近特征线和特征面分类器,可直接对高维人脸图像进行识别.为解决计算量大和可能失效的问题,提出(核)最近特征重心和(核)最近邻特征两种解决方法,前者降低了计算特征线和面距离的复杂度,后者减少了特征线和面的数目,两种方法均避免了可能失效的问题.将二者结合得到的(核)最近邻特征重心分类器,在获得相近识别率的条件下,使计算复杂度降到了最小.所得方法无需预先抽取人脸图像特征,因此避免了在较多样本数时,特征抽取存在计算量大的问题.基于ORL人脸数据库的实验验证了本文方法的有效性.     

Fast nearest neighbour testing algorithm for small feature sizes

A fast nearest neighbour algorithm with a logarithmic expected testing time for small feature sizes is presented. It has been tested on a robot vision application for YUV space. The algorithm time requirement is better than a multilayer perceptron trained for the same purpose.     

Generalised scheme for optimal learning in recurrent neuralnetworks

A new learning scheme is proposed for neural network architectures like the Hopfield network and bidirectional associative memory. This scheme, which replaces the commonly used learning rules, follows from the proof of the result that learning in these connectivity architectures is equivalent to learning in the 2-state perceptron. Consequently, optimal learning algorithms for the perceptron can be directly applied to learning in these connectivity architectures. Similar results are established for learning in the multistate perceptron, thereby leading to an optimal learning algorithm. Experimental results are provided to show the superiority of the proposed method     

Generalised distributions of nonuniformity of hypergeometric transmission line

This letter outlines a general procedure for deriving the generalised expressions for nonuniformity patterns of all classes of solvable transmission lines, taking a class of hyper-geometric lines as an example for illustrating the application of the procedure.     

无监督行人重识别的判别性特征研究

目前,在计算机视觉方面,大多的监督学习方法用于解决其重要分支:行人重识别问题已经取得了不错的成果,但是此类方法需要对训练数据进行手工标注,特别是对于大容量的数据集,手工标注的成本很高,而且完全满足成对标记的数据难以获得,所以无监督学习成为必选项.此外,全局特征注重行人特征空间整体性的判别性,而局部特征有助于凸显不同部位特征的判别性.所以,基于全局与局部特征的无监督学习框架,使用全局损失函数与局部相斥损失函数共同进行判别性特征学习,并联合优化ResNet-50卷积神经网络(CNN)和各个样本之间的关系,最终实现行人重识别.大量实验数据验证了提出的方法在解决行人重识别任务时具有优越性.     

A robust elastic net approach for feature learning

Unsupervised feature learning has drawn more and more attention especially in visual representation in past years. Traditional feature learning approaches assume that there are few noises in training data set, and the number of samples is enough compared with the dimensions of samples. Unfortunately, these assumptions are violated in most of visual representation scenarios. In these cases, many feature learning approaches are failed to extract the important features. Toward this end, we propose a Robust Elastic Net (REN) approach to handle these problems. Our contributions are twofold. First of all, a novel feature learning approach is proposed to extract features by weighting elastic net. A distribution induced weight function is used to leverage the importance of different samples thus reducing the effects of outliers. Moreover, the REN feature learning approach can handle High Dimension, Low Sample Size (HDLSS) issues. Second, a REN classifier is proposed for object recognition, and can be used for generic visual representation including that from the REN feature extraction. By doing so, we can reduce the effect of outliers in samples. We validate the proposed REN feature learning and classifier on face recognition and background reconstruction. The experimental results showed the robustness of this proposed approach for both corrupted/occluded samples and HDLSS issues.     

Kernel propagation strategy: A novel out-of-sample propagation projection for subspace learning

Kernel matrix optimization (KMO) aims at learning appropriate kernel matrices by solving a certain optimization problem rather than using empirical kernel functions. Since KMO is difficult to compute out-of-sample projections for kernel subspace learning, we propose a kernel propagation strategy (KPS) based on data distribution similar principle to effectively extract out-of-sample low-dimensional features for subspace learning with KMO. With KPS, we further present an example algorithm, i.e., kernel propagation canonical correlation analysis (KPCCA), which naturally fuses semi-supervised kernel matrix learning and canonical correlation analysis by means of kernel propagation projections. In KPCCA, the extracted correlation features of out-of-sample data not only incorporate integral data distribution information but also supervised information. Extensive experimental results have demonstrated the superior performance of our proposed method.     

说话人语音特征子空间分离及识别应用

本文依据主元分析原理从语音特征观察空间分离说话人语音特征子空间,对输入语音特征矢量与子空间的距离测度进行了定义,并对基于特征子空间的说话人识别性能进行了分析.说话人语音训练样本提取特征后在语音特征观察空间形成具有一定散度的分布,根据主元分析原理和分布散度提取主要散度本征向量作为基底构成说话人语音特征子空间,并通过测试语音特征矢量与子空间的距离测度进行模式匹配.实验结果表明,特征子空间方法对说话人识别是有效的,特别是在小于3秒的短时测试语音下能够得到较高的识别率.     

Generalised theory of a non-TEM transmission line taper without discontinuities

The optimum Chebyshev dispersive tapered transmission line requires impedance steps at the taper ends, which limits its application. A generalised theory for the synthesis of a transmission line taper without impedance steps and supporting non-TEM modes is presented. The impedance discontinuities are avoided by using a near-optimum frequency response.<>     

Tunable line spectral estimators based on state-covariance subspace analysis

Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator.     

Nearest neighbour line nonparametric discriminant analysis for feature extraction

A new feature extraction method, called nearest neighbour line nonparametric discriminant analysis (NNL-NDA), is proposed. The previous nonparametric discriminant analysis methods only use point-to-point distance to measure the class difference. In NNL-NDA, point-to-line distance with nearest neighbour line (NNL) theory is adopted, and thereby more intrinsic structure information of training samples is preserved in the feature space. NNL-NDA does not assume that the class densities belong to any particular parametric family nor encounter the singularity difficulty of the within-class scatter matrix. Experimental results on ORL face database demonstrate the effectiveness of the proposed method.     

Multi-patch embedding canonical correlation analysis for multi-view feature learning

Locality-based feature learning for multi-view data has received intensive attention recently. As a result of only considering single-category local neighbor relationships, most of such the learning methods are difficult to well reveal intrinsic geometric structure information of raw high-dimensional data. To solve the problem, we propose a novel supervised multi-view correlation feature learning algorithm based on multi-category local neighbor relationships, called multi-patch embedding canonical correlation analysis (MPECCA). Our algorithm not only employs multiple local patches of each raw data to better capture the intrinsic geometric structure information, but also makes intraclass correlation features as close as possible by minimizing intraclass scatter of each view. Extensive experimental results on several real-world image datasets have demonstrated the effectiveness of our algorithm.     

An orthogonal regularized CCA learning algorithm for feature fusion

Canonical correlation analysis (CCA) aims at extracting statistically uncorrelated features via conjugate orthonormalization constraints of the projection directions. However, the formulated directions under conjugate orthonormalization are not reliable when the training samples are few and the covariance matrix has not been exactly estimated. Additionally, this widely pursued property is focused on data representation rather than task discrimination. It is not suitable for classification problems when the samples that belong to different classes do not share the same distribution type. In this paper, an orthogonal regularized CCA (ORCCA) is proposed to avoid the above questions and extract more discriminative features via orthogonal constraints and regularized parameters. Experimental results on both handwritten numerals and face databases demonstrate that our proposed method significantly improves the recognition performance.     

Generalised solutions for distributed lines

Attention is drawn to the fact that previous classifications o generalised exact closed-form solutions for distributed lines are incomplete. Equations are presented which could form the basis for such a classification.     

Fast subspace tracking and neural network learning by a novelinformation criterion

We introduce a novel information criterion (NIC) for searching for the optimum weights of a two-layer linear neural network (NN). The NIC exhibits a single global maximum attained if and only if the weights span the (desired) principal subspace of a covariance matrix. The other stationary points of the NIC are (unstable) saddle points. We develop an adaptive algorithm based on the NIC for estimating and tracking the principal subspace of a vector sequence. The NIC algorithm provides a fast on-line learning of the optimum weights for the two-layer linear NN. We establish the connections between the NIC algorithm and the conventional mean-square-error (MSE) based algorithms such as Oja's algorithm (Oja 1989), LMSER, PAST, APEX, and GHA. The NIC algorithm has several key advantages such as faster convergence, which is illustrated through analysis and simulation     

基于子空间半监督学习线性判别方法的目标跟踪技术研究

文中提出一种基于半监督学习的线性判别方法用于目标跟踪。首先,根据少量的目标图像和背景图像样本,利用增量线性判别分析在子空间中找到最大化标记样本分类间隔的分类面;然后在当前帧采样,获得大量未标记的图像样本并投影到子空间中,通过半监督学习修正分类面,在这些候选目标中找到离目标最近、离背景最远的作为目标在当前帧的状态估计;最后,在分类结果中挑选置信度高的目标图像和背景图像样本加入到训练集中,删除训练集中置信度低的目标图像和背景图像样本,并更新投影子空间的基。实验结果表明,所提方法可以很好地适应目标的各种变化,并获得比基于监督学习方法更好的效果。