Color image canonical correlation analysis for face feature extraction and recognition

Canonical correlation analysis (CCA) is a powerful statistical analysis technique, which can extract canonical correlated features from two data sets. However, it cannot be directly used for color images that are usually represented by three data sets, i.e., red, green and blue components. Current multi-set CCA (mCCA) methods, on the other hand, can only provide the iterative solutions, not the analytical solutions, when processing multiple data sets. In this paper, we develop the CCA technique and propose a color image CCA (CICCA) approach, which can extract canonical correlated features from three color components and provide the analytical solution. We show the mathematical model of CICCA, prove that CICCA can be cast as solving three eigen-equations, and present the realization algorithm of CICCA. Experimental results on the AR and FRGC-2 public color face image databases demonstrate that CICCA outperforms several representative color face recognition methods.     

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.     

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.     

基于区域特征与灰度交叉相关的序列图像拼接

针对具有平移、旋转、缩放变换的序列图像连续拼接,提出一种将基于区域特征的配准算法和基于灰度交叉相关的配准算法相结合的拼接算法。该算法用迭代阈值分割算法提取区域．利用区域特征进行配准,建立初始的同名区域对;然后以同名区域对的质心点作为特征点,基于图像的灰度信息,选择交叉相关准则作为度量;最后得到图像间的精确变换关系,实现序列图像的拼接。实验结果表明,算法配准率高,鲁棒性强。     

基于相关分析的飞机目标识别方法

提出了一种基于相关分析的飞机目标识别方法。该方法利用飞机图像低频和高频部分合成滤波器模板,能达到很高识别率与很低的等错率。该研究旨在提高飞机识别的准确率和降低出错率,采用一种基于相关分析的飞机目标识别方法。该方法通过对采集的飞机图像做去除背景、降噪、图像增强、二值化和归一化处理,将飞机图像低频和高频部分合成滤波器模板,通过特征比对达到识别飞机的目的。利用Matlab 7.0做10种飞机的识别实验,得出了95.47%识别率和0.04%等错率的结论,识别率和等错率均优于不变矩法、三维识别方法、基于小波分析和矩不变量的方法,印证了笔者提出的基于相关分析的飞机目标识别方法的优越性。在飞机图像数据库上的实验结果表明,该方法是可行的。     

Canonical principal angles correlation analysis for two-view data

Canonical correlation analysis (CCA) is a popular method that has been widely used in information fusion. However, CCA requires that the data from two views must be paired, which is hard to satisfy in the real applications, moreover, it only considers the correlated information of the paired data. Thus, it cannot be used when there are only a little paired data or no paired data. In this paper, we propose a novel method named Canonical Principal Angles Correlation Analysis (CPACA) which does not need paired data during training stage. It makes classic CCA escape from the limitation of paired information. Its objective function can be constructed as follows: First, the correlation of two views is represented by the similarity between two subspace spanned by the principal components, which makes CPACA favorably compare with CCA in the case of limited paired data; Second, in order to increase the discriminative information of CPACA, we utilize manifold regularization to exploit the geometry of the marginal distribution. To optimize the objective function, we propose a new method to calculate the projected vectors. The experimental results show that the performance of CPACA is superior to that of traditional CCA and its variants.     

面向图像识别的测地局部典型相关分析方法

典型相关分析(CCA)是一种经典的多模态特征学习方法,能够从不同模态同时学习相关性最大的低维特征,然而难以发现隐藏在样本空间中的非线性流形结构。该文提出一种基于测地流形的多模态特征学习方法,即测地局部典型相关分析(GeoLCCA)。该方法利用测地距离构建了低维相关特征的测地散布,并进一步通过最大化模态间的相关性和最小化模态内的测地散布学习更具鉴别力的非线性相关特征。该文不仅在理论上对提出的方法进行了分析,而且在真实的图像数据集上验证了方法的有效性。     

Multiple kernel learning with NOn-conVex group spArsity

As the high-dimensional heterogeneous visual features extracted from images are intrinsically embedded in a non-linear space, some kernel methods such as SVM have been proposed to solve this problem. Since different kinds of heterogeneous features in images have different intrinsic discriminative powers for image understanding, how to enforce grouping sparsity penalty to effectively select out discriminative heterogeneous visual features is critical for image understanding. Most existing approaches are using a convex penalty for feature selection, which easily leads to inconsistent selection. To guarantee a consistent selection for heterogeneous features embedded in a non-linear space, this paper proposes a new approach called MKL-NOVA (Multiple Kernel Learning with NOn-conVex group spArsity). Because MKL-NOVA conducts a non-convex penalty for the selection of groups of features, it achieves the consistent selection. Furthermore, considering the contextual correlation between multi labels, sparse canonical correlation analysis is conducted to boost the image annotation performance by MKL-NOVA. We have demonstrated the superior performance of MKL-NOVA via two experiments in the paper. First, we showed that MKL-NOVA converges to the true underlying model by using a ground-truth-available generative-model simulation. Second, we compare the proposed MKL-NOVA and the state-of-the-art approaches which showed that MKL-NOVA achieved the best performance.     

基于小波分析的JEM特征提取与应用

根据对常规防空雷达飞机回波特性及其JEM(Jet Engine Modulation)特征的理论模型和实际信号分析与理解,本文采用小波分析处理方法从回波幅度分量提取JEM特征,用于螺旋桨飞机自动识别。对实测回波数据的分析提取到了比较精确而稳定的JEM特征,并表明该方法具有提取方便、误差较小的优点。     

光学相关识别中基于傅里叶分析的特征提取算法研究

光学相关识别中基于傅里叶分析方法实现了同一类相似目标的共同特征提取.计算机模拟和实验结果表明用提取特征编码的匹配滤波器能同时实现对两目标的识别.     

空间一致性邻域保留嵌入的高光谱数据特征提取

局部线性嵌入(LLE)和邻域保留嵌入(NPE)等流形学习方法可以提取高光谱数据的主要结构特征,有助于对数据的理解和进一步处理。但是,这些方法忽视了高光谱图像中相邻像素之间的相关性。针对这个问题,提出一种基于空间一致性思想的邻域保留嵌入(SC-NPE)特征提取算法,通过一个优化的局部线性嵌入,并考虑相邻像素的相关特性,在高维空间建立数据的局部邻域结构。然后寻找一个优化的变换矩阵,将局部邻域结构投影到低维空间,实现数据的特征提取。与LLE和NPE算法相比,SC-NPE既考虑高光谱数据的流形结构,又考虑了其图像域空间信息,可以更好地应用在高光谱数据的特征提取过程中。实验结果表明,SC-NPE特征提取算法在高光谱图像分类方面的性能明显优于其他同类算法。     

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.     

语音信号中的情感特征分析和识别的研究

本文分析了含有欢快、愤怒、惊奇、悲伤等４种情感语音信号的时间构造、振幅构造、基频构造和共振峰构造的特征。通过和不带情感的平静语音信号的比较,总结了不同情感语音信号的情感特征的分布规律。根据这些分析,提取了９个情感特征进行了情感识别的实验,获得了基本上接近于人的正常表现的识别结果。     

No embedding: A novel image cryptosystem for meaningful encryption

In this paper, we propose a novel image cryptosystem, which enables to encrypt the secret images with a smaller-size cover image. Compared with the existing meaningful encryption methods, our cryptosystem has three advantages: (1) non-embedding encryption, i.e., there isn’t any data embedding into the cover image during the encryption process. (2) Our cryptosystem can simultaneously encrypt multiple secret images with one cover image, which greatly improves the security of secret images. (3) Our cryptosystem can accomplish not only the meaningful encryption, but also the meaningless encryption. Thus, people don’t switch encryption methods when meeting different encryption requirements. Our scheme leverages the popular coupled dictionary learning and compressive sensing techniques to accomplish the whole task. Specifically, we use the coupled dictionaries to build connection between the cover image and the secret image, and apply the compressive sensing to decrypt the secret image. To demonstrate the effectiveness of the proposed cryptosystem, a series of experiments are conducted. Experimental results on gray images and colorful RGB images verify its superiority.     

Local maximal margin discriminant embedding for face recognition

In this paper, a manifold learning based method named local maximal margin discriminant embedding (LMMDE) is developed for feature extraction. The proposed algorithm LMMDE and other manifold learning based approaches have a point in common that the locality is preserved. Moreover, LMMDE takes consideration of intra-class compactness and inter-class separability of samples lying in each manifold. More concretely, for each data point, it pulls its neighboring data points with the same class label towards it as near as possible, while simultaneously pushing its neighboring data points with different class labels away from it as far as possible under the constraint of locality preserving. Compared to most of the up-to-date manifold learning based methods, this trick makes contribution to pattern classification from two aspects. On the one hand, the local structure in each manifold is still kept in the embedding space; one the other hand, the discriminant information in each manifold can be explored. Experimental results on the ORL, Yale and FERET face databases show the effectiveness of the proposed method.     

Intrinsic structure based feature transform for image classification

Most dimensionality reduction works construct the nearest-neighbor graph by using Euclidean distance between images; this type of distance may not reflect the intrinsic structure. Different from existing methods, we propose to use sets as input rather than single images for accurate distance calculation. The set named as neighbor circle consists of the corresponding data point and its neighbors in the same class. Then a supervised dimensionality reduction method is developed, i.e., intrinsic structure feature transform (ISFT), it captures the local structure by constructing the nearest-neighbor graph using the Log-Euclidean distance as measurements of neighbor circles. Furthermore, ISFT finds representative images for each class; it captures the global structure by using the projected samples of these representatives to maximize the between-class scatter measure. The proposed method is compared with several state-of-the-art dimensionality reduction methods on various publicly available databases. Extensive experimental results have demonstrated the effectiveness of the proposed approach.     

Reversible data embedding for indices based on histogram analysis

Reversible data hiding is a technique that not only protects the hidden secrets but also recovers the cover media without any distortion after the secret data have been extracted. In this paper, a new reversible data hiding technique for VQ indices which are compressed streams based on the mapping function and histogram analysis of transformed VQ indices is introduced to enhance the performance of some earlier reversible data hiding schemes that are based on VQ indices. As a result, the proposed scheme achieves high embedding capacity and data compression simultaneously. Moreover, the original VQ-compressed image can be perfectly reconstructed after secret data extraction. To estimate the performance of the proposed scheme, variety of test images are used in the experimental testing. As can be seen in the experimental result, our scheme is superior to some previous schemes in term of compression rate and embedding rate while maintaining the reversibility.