基于虚拟样本图像集的多流形鉴别学习算法

为了丰富训练样本的类内变化信息,提出了基于通用训练样本集的虚拟样本生成方法。进一步,为了利用生成的虚拟样本中的类内变化信息有效地完成单样本人脸识别任务,提出了基于虚拟样本图像集的多流行鉴别学习算法。该算法首先将每类仅有的单个训练样本图像和该类的虚拟样本图像划分为互补重叠的局部块并构建流形,然后为每个流形学习一个投影矩阵,使得相同流形内的局部块在投影后的低维特征空间间隔最小化,不同流形中的局部块在投影后的低维特征空间中间隔最大化。实验结果表明,所提算法能够准确地预测测试样本中的类内变化,是一种有效的单样本人脸识别算法。     

Online nonparametric discriminant analysis for incremental subspace learning and recognition

This paper presents a novel approach for online subspace learning based on an incremental version of the nonparametric discriminant analysis (NDA). For many real-world applications (like the study of visual processes, for instance) it is impossible to know beforehand the number of total classes or the exact number of instances per class. This motivated us to propose a new algorithm, in which new samples can be added asynchronously, at different time stamps, as soon as they become available. The proposed technique for NDA-eigenspace representation has been used in pattern recognition applications, where classification of data has been performed based on the nearest neighbor rule. Extensive experiments have been carried out both in terms of classification accuracy and execution time. On the one hand, the results show that the Incremental NDA converges towards the classical NDA at the end of the learning process and furthermore. On the other hand, Incremental NDA is suitable to update a large knowledge representation eigenspace in real-time. Finally, the use of our method on a real-world application is presented.     

Social image annotation via cross-domain subspace learning

In recent years, cross-domain learning algorithms have attracted much attention to solve labeled data insufficient problem. However, these cross-domain learning algorithms cannot be applied for subspace learning, which plays a key role in multimedia processing. This paper envisions the cross-domain discriminative subspace learning and provides an effective solution to cross-domain subspace learning. In particular, we propose the cross-domain discriminative locally linear embedding or CDLLE for short. CDLLE connects the training and the testing samples by minimizing the quadratic distance between the distribution of the training samples and that of the testing samples. Therefore, a common subspace for data representation can be preserved. We basically expect the discriminative information to separate the concepts in the training set can be shared to separate the concepts in the testing set as well and thus we have a chance to address above cross-domain problem duly. The margin maximization is duly adopted in CDLLE so the discriminative information for separating different classes can be well preserved. Finally, CDLLE encodes the local geometry of each training samples through a series of linear coefficients which can reconstruct a given sample by its intra-class neighbour samples and thus can locally preserve the intra-class local geometry. Experimental evidence on NUS-WIDE, a popular social image database collected from Flickr, and MSRA-MM, a popular real-world web image annotation database collected from the Internet by using Microsoft Live Search, demonstrates the effectiveness of CDLLE for real-world cross-domain applications.     

Tensor-driven low-rank discriminant analysis for image set classification

Multimedia Tools and Applications - Classification based on image sets has recently attracted great interest in computer vision community. In this paper, we proposed a transductive Tensor-driven...     

基于子空间学习的快速自适应局部比值和判别分析

降维是处理高维数据的一项关键技术,其中线性判别分析及其变体算法均为有效的监督算法。然而大多数判别分析算法存在以下缺点：a)无法选择更具判别性的特征;b)忽略原始空间中噪声和冗余特征的干扰;c)更新邻接图的计算复杂度高。为了克服以上缺点,提出了基于子空间学习的快速自适应局部比值和判别分析算法。首先,提出了统一比值和准则及子空间学习的模型,以在子空间中探索数据的潜在结构,选择出更具判别信息的特征,避免受原始空间中噪声的影响;其次,采用基于锚点的策略构造邻接图来表征数据的局部结构,加速邻接图学习;然后,引入香农熵正则化,以避免平凡解;最后,在多个数据集上进行了对比实验,验证了算法的有效性。     

Representation learning with deep extreme learning machines for efficient image set classification

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

     

Coupled dictionary learning method for image decomposition

A novel variational model for image decomposition is proposed.Meanwhile a new cartoon-texture dictionary learning algorithm,which is guided by diffusion flow,is presented.Numerical experiments show that the proposed method has better performance than the existing algorithms in image decomposition and denoising.     

一种具有统计不相关性的广义最优判别向量集

提出了一种新的最优判别向量集即统计不相关广义最优判别向量集 ,并给出了计算公式。用ORL人脸数据库进行人脸识别实验 ,结果表明该方法有较强的特征提取能力。     

A discriminant sparse representation graph-based semi-supervised learning for hyperspectral image classification

Multimedia Tools and Applications - The classification of hyperspectral image with a paucity of labeled samples is a challenging task. In this paper, we present a discriminant sparse representation...     

Face recognition using second-order discriminant tensor subspace analysis

Discriminant information (DI) plays a critical role in face recognition. In this paper, we proposed a second-order discriminant tensor subspace analysis (DTSA) algorithm to extract discriminant features from the intrinsic manifold structure of the tensor data. DTSA combines the advantages of previous methods with DI, the tensor methods preserving the spatial structure information of the original image matrices, and the manifold methods preserving the local structure of the samples distribution. DTSA defines two similarity matrices, namely within-class similarity matrix and between-class similarity matrix. The within-class similarity matrix is determined by the distances of point pairs in the same class, while the between-class similarity matrix is determined by the distances between the means of each pair of classes. Using these two matrices, the proposed method preserves the local structure of the samples to fit the manifold structure of facial images in high dimensional space better than other methods. Moreover, compared to the 2D methods, the tensor based method employs two-sided transformations rather than single-sided one, and yields higher compression ratio. As a tensor method, DTSA uses an iterative procedure to calculate the optimal solution of two transformation matrices. In this paper, we analyzed DTSA's connections to 2D-DLPP and TSA, theoretically. The experiments on the ORL, Yale and YaleB facial databases show the effectiveness of the proposed method.     

Nonparametric discriminant analysis via recursive optimization ofPatrick-Fisher distance

A method for the linear discrimination of two classes is presented. It searches for the discriminant direction which maximizes the Patrick-Fisher (PF) distance between the projected class-conditional densities. It is a nonparametric method, in the sense that the densities are estimated from the data. Since the PF distance is a highly nonlinear function, we propose a recursive optimization procedure for searching the directions corresponding to several large local maxima of the PF distance. Its novelty lies in the transformation of the data along a found direction into data with deflated maxima of the PF distance and iteration to obtain the next direction. A simulation study and a medical data analysis indicate the potential of the method to find the sequence of directions with significant class separations.     

Convergence analysis of instrumental variable recursive subspace identification algorithms

The convergence properties of recently developed recursive subspace identification methods are investigated in this paper. The algorithms operate on the basis of instrumental variable (IV) versions of the propagator method for signal subspace estimation. It is proved that, under suitable conditions on the input signal and the system, the considered recursive subspace identification algorithms converge to a consistent estimate of the propagator and, by extension, to the state-space system matrices.     

Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm

In this paper, a novel spectral-spatial hyperspectral image classification method has been proposed by designing hierarchical subspace switch ensemble learning algorithm. First, the hyperspectral images are processed by fast bilateral filtering to get the spatial features. The spectral features and spatial features are combined to form the initial feature set. Second, Hierarchical instance learning based on iterative means clustering method is designed to obtain hierarchical instance space. Third, random subspace method (RSM) is used for sampling the features and samples, thereby forming multiple sub sample set. After that, semi-supervised learning (S²L) is applied to choose test samples for improving classification performance without touching the class labels. Then, micro noise linear dimension reduction (mNLDR) is used for dimension reduction. Afterwards, ensemble multiple kernels SVM(EMK_SVM) are used for stable classification results. Finally, final classification results are obtained by combining classification results with voting strategy. Experimental results on real hyperspectral scenes demonstrate that the proposed method can effectively improve the classification performance apparently.     

Learning shared subspace regularization with linear discriminant analysis for multi-label action recognition

The Journal of Supercomputing - Human action recognition under complex environment is a challenging work, while in deep learning and in these specific difficulty recognition tasks, the multi-label...     

完备的双子空间边界近邻鉴别分析

针对边界Fisher鉴别分析算法不能够有效解决小样本问题,提出了一种完备的双子空间边界近邻鉴别分析算法。该算法通过理论分析将MFA的目标函数分解成两部分,对此目标函数的求解,首先要对高维样本进行PCA降维至一个低维子空间, 而这一过程并不损失任何有效的鉴别信息,对此通过定理1和定理2进行了证明;然后再分别求出类内边界近邻互补子空间的两投影矩阵。最后人脸库上的实验结果表明了所提方法的有效性。     

A new discriminant subspace analysis approach for multi-class problems

Fukunaga–Koontz Transform (FKT) is a famous feature extraction method in statistical pattern recognition, which aims to find a set of vectors that have the best representative power for one class while the poorest representative power for the other class. Li and Savvides [1] propose a one-against-all strategy to deal with multi-class problems, in which the two-class FKT method can be directly applied to find the presentative vectors of each class. Motivated by the FKT method, in this paper we propose a new discriminant subspace analysis (DSA) method for the multi-class feature extraction problems. To solve DSA, we propose an iterative algorithm for the joint diagonalization (JD) problem. Finally, we generalize the linear DSA method to handle nonlinear feature extraction problems via the kernel trick. To demonstrate the effectiveness of the proposed method for pattern recognition problems, we conduct extensive experiments on real data sets and show that the proposed method outperforms most commonly used feature extraction methods.     

融合判别式深度特征学习的图像识别算法

目的 卷积神经网络在图像识别算法中得到了广泛应用。针对传统卷积神经网络学习到的特征缺少更有效的鉴别能力而导致图像识别性能不佳等问题,提出一种融合线性判别式思想的损失函数LDloss（linear discriminant loss）并用于图像识别中的深度特征提取,以提高特征的鉴别能力,进而改善图像识别性能。方法 首先利用卷积神经网络搭建特征提取所需的深度网络,然后在考虑样本分类误差最小化的基础上,对于图像多分类问题,引入LDA（linear discriminant analysis）思想构建新的损失函数参与卷积神经网络的训练,来最小化类内特征距离和最大化类间特征距离,以提高特征的鉴别能力,从而进一步提高图像识别性能,分析表明,本文算法可以获得更有助于样本分类的特征。其中,学习过程中采用均值分批迭代更新的策略实现样本均值平稳更新。结果 该算法在MNIST数据集和CK+数据库上分别取得了99.53%和94.73%的平均识别率,与现有算法相比较有一定的提升。同时,与传统的损失函数Softmax loss和Hinge loss对比,采用LDloss的深度网络在MNIST数据集上分别提升了0.2%和0.3%,在CK+数据库上分别提升了9.21%和24.28%。结论 本文提出一种新的融合判别式深度特征学习算法,该算法能有效地提高深度网络的可鉴别能力,从而提高图像识别精度,并且在测试阶段,与Softmax loss相比也不需要额外的计算量。     

一种无约束的最优判别向量集

在正交约束条件下,求使Fisher准则判别函数式取极大值的向量,这样的最优判别向量就是F-S最优判别向量集。基于Fisher判别准则函数式,提出了一种无约束的最优判别矢量集,并给出了求解算法。另外,当训练样本矢量数小于样本矢量维数（即小样本问题）,类内散布矩阵奇异,为了使它非奇异,采取对样本进行降维的措施,那维数至少要降到多少维才能确保它非奇异,给出了计算公式。实验结果表明鉴别矢量集有良好的分类能力。