基于块对角化表示的多视角字典对学习

字典学习作为一种高效的特征学习技术被广泛应用于多视角分类中.现有的多视角字典学习方法大多只利用多视角数据的部分信息,且只学习一种类型的字典.实际上,多视角数据的相关性信息和多样性信息同样重要,且仅考虑一种合成型字典或解析型字典的学习算法不能同时满足处理速度、可解释性以及应用范围的要求.针对上述问题,提出了一种基于块对角...     

Generative multi-view and multi-feature learning for classification

Multi-view based classification has attracted much attention in recent years. In general, an object can be represented with various views or modalities, and the exploitation of correlation across different views would contribute to improving the classification performance. However, each view can also be described with multiple features and this types of data is called multi-view and multi-feature data. Different from many existing multi-view methods which only model multiple views but ignore intrinsic information among the various features in each view, a generative bayesian model is proposed in this paper to not only jointly take the features and views into account, but also learn a discriminant representation across distinctive categories. A latent variable corresponding to each feature in each view is assumed and the raw feature is a projection of the latent variable from a more discriminant space. Particularly, the extracted variables in each view belonging to the same class are encouraged to follow the same gaussian distribution and those belonging to different classes are conducted to follow different distributions, greatly exploiting the label information. To optimize the presented approach, the proposed method is transformed into a class-conditional model and an effective algorithm is designed to alternatively estimate the parameters and variables. The experimental results on the extensive synthetic and four real-world datasets illustrate the effectiveness and superiority of our method compared with the state-of-the-art.     

A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples

Correlated information between multiple views can provide useful information for building robust classifiers. One way to extract correlated features from different views is using canonical correlation analysis (CCA). However, CCA is an unsupervised method and can not preserve discriminant information in feature extraction. In this paper, we first incorporate discriminant information into CCA by using random cross-view correlations between within-class examples. Because of the random property, we can construct a lot of feature extractors based on CCA and random correlation. So furthermore, we fuse those feature extractors and propose a novel method called random correlation ensemble (RCE) for multi-view ensemble learning. We compare RCE with existing multi-view feature extraction methods including CCA and discriminant CCA (DCCA) which use all cross-view correlations between within-class examples, as well as the trivial ensembles of CCA and DCCA which adopt standard bagging and boosting strategies for ensemble learning. Experimental results on several multi-view data sets validate the effectiveness of the proposed method.     

基于局部流形重构的半监督多视图图像分类

为了在半监督情境下利用多视图特征中的信息提升分类性能,通过最小化输入特征向量的局部重构误差为以输入特征向量为顶点构建的图学习合适的边权重,将其用于半监督学习。通过将最小化输入特征向量的局部重构误差捕获到的输入数据的流形结构应用于半监督学习,有利于提升半监督学习中标签预测的准确性。对于训练样本图像的多视图特征的使用问题,借助于改进的典型相关分析技术学习更具鉴别性的多视图特征,将其有效融合并用于图像分类任务。实验结果表明,该方法能够在半监督情境下充分地挖掘训练样本的多视图特征表示的鉴别信息,有效地完成鉴别任务。     

面向自闭症辅助诊断的无监督模糊特征学习新方法

自闭症患者的行为和认知缺陷与潜在的脑功能异常有关。对于静息态功能磁振图像（functional magnetic resonance imaging, fMRI）高维特征,传统的线性特征提取方法不能充分提取其中的有效信息用于分类。为此,本文面向fMRI数据提出一种新型的无监督模糊特征映射方法,并将其与多视角支持向量机相结合,构建分类模型应用于自闭症的计算机辅助诊断。该方法首先采用多输出TSK模糊系统的规则前件学习方法,将原始特征数据映射到线性可分的高维空间;然后引入流形正则化学习框架,提出新型的无监督模糊特征学习方法,从而得到原输出特征向量的非线性低维嵌入表示;最后使用多视角SVM算法进行分类。实验结果表明:本文方法能够有效提取静息态fMRI数据中的重要特征,在保证模型具有优越且稳定的分类性能的前提下,还可以提高模型的可解释性。     

基于多类最大散度差的人脸表示方法

将用于两类分类的最大散度差鉴别准则推广为多类最大散度差鉴别准则,并建立了基于该准则的一种新的人脸表示方法.基于多类最大散度差鉴别准则的人脸表示方法有效避免了传统鉴别分析方法在人脸特征提取时通常面临的小样本模式识别问题.在国际标准人脸图像数据库ORL、Yale以及FERET上的实验结果表明,与Fisherfaces、Eigenfaces、正交补空间、零空间等人脸特征提取方法相比,新的人脸表示方法具有一定的优势.     

Multi-view dimensionality reduction via canonical random correlation analysis

Canonical correlation analysis (CCA) is one of the most well-known methods to extract features from multi-view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into account. In this paper, we add discriminant information into CCA by using random cross-view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis (RCA). In RCA, two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique. Furthermore, kernel RCA (KRCA) is proposed to extract nonlinear correlations between different views. Experiments on several multi-view data sets show the effectiveness of the proposed methods.     

Feature extraction using orthogonal discriminant local tangent space alignment

A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.     

A fast classification scheme and its application to face recognition

To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called ’reconstruction proportion’ is exploited to reflect the discriminant information. A novel space called the ’reconstruction space’ is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.     

Open-view human action recognition based on linear discriminant analysis

In the last decades, action recognition task has evolved from single view recording to unconstrained environment. Recently, multi-view action recognition has become a hot topic in computer vision. However, we notice that only a few works have focused on the open-view action recognition, which is a common problem in the real world. Open-view action recognition focus on doing action recognition in unseen view without using any information from it. To address this issue, we firstly introduce a novel multi-view surveillance action dataset and benchmark several state-of-the-art algorithms. From the results, we observe that the performance of the state-of-the-art algorithms would drop a lot under open-view constraints. Then, we propose a novel open-view action recognition method based on the linear discriminant analysis. This method can learn a common space for action samples under different view by using their category information, which can achieve a good result in open-view action recognition.

     

Joint dynamic sparse representation for multi-view face recognition

We consider the problem of automatically recognizing a human face from its multi-view images with unconstrained poses. We formulate the multi-view face recognition task as a joint sparse representation model and take advantage of the correlations among the multiple views for face recognition using a novel joint dynamic sparsity prior. The proposed joint dynamic sparsity prior promotes shared joint sparsity patterns among the multiple sparse representation vectors at class-level, while allowing distinct sparsity patterns at atom-level within each class to facilitate a flexible representation. Extensive experiments on the CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method.     

Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image

We present a novel method of nonlinear discriminant analysis involving a set of locally linear transformations called "Locally Linear Discriminant Analysis" (LLDA). The underlying idea is that global nonlinear data structures are locally linear and local structures can be linearly aligned. Input vectors are projected into each local feature space by linear transformations found to yield locally linearly transformed classes that maximize the between-class covariance while minimizing the within-class covariance. In face recognition, linear discriminant analysis (LIDA) has been widely adopted owing to its efficiency, but it does not capture nonlinear manifolds of faces which exhibit pose variations. Conventional nonlinear classification methods based on kernels such as generalized discriminant analysis (GDA) and support vector machine (SVM) have been developed to overcome the shortcomings of the linear method, but they have the drawback of high computational cost of classification and overfitting. Our method is for multiclass nonlinear discrimination and it is computationally highly efficient as compared to GDA. The method does not suffer from overfitting by virtue of the linear base structure of the solution. A novel gradient-based learning algorithm is proposed for finding the optimal set of local linear bases. The optimization does not exhibit a local-maxima problem. The transformation functions facilitate robust face recognition in a low-dimensional subspace, under pose variations, using a single model image. The classification results are given for both synthetic and real face data.     

Fuzzy discriminant analysis with kernel methods

A novel fuzzy nonlinear classifier, called kernel fuzzy discriminant analysis (KFDA), is proposed to deal with linear non-separable problem. With kernel methods KFDA can perform efficient classification in kernel feature space. Through some nonlinear mapping the input data can be mapped implicitly into a high-dimensional kernel feature space where nonlinear pattern now appears linear. Different from fuzzy discriminant analysis (FDA) which is based on Euclidean distance, KFDA uses kernel-induced distance. Theoretical analysis and experimental results show that the proposed classifier compares favorably with FDA.     

基于WLDA和i-稀疏表示分类的说话人确认

为了提高信道变化下说话人确认系统的识别率和鲁棒性,提出一种基于i-向量和加权线性判别分析的稀疏表示分类算法。首先借助于加权线性判别分析的信道补偿和降维性能,消除i-向量中信道干扰信息并降低i-向量的维数;紧接着在i-向量集上构建训练语音样本过完备字典矩阵,采用MAP算法求解测试语音在字典矩阵上的稀疏系数向量,最后利用稀疏系数向量重构测试语音样本,根据重构误差确定目标说话人。仿真实验结果验证了该算法的有效性和可行性。     

Multi-view transition HMMs based view-invariant human action recognition method

View-invariant human action recognition is a challenging research topic in computer vision. Hidden Markov Models(HMM) and their extensions have been widely used for view-invariant action recognition. However those methods are usually according to a large parameter space, requiring amounts of training data and with low classification accuracies for real application. A novel graphical structure based on HMM with multi-view transition is proposed to model the human action with viewpoint changing. The model consists of multiple sub action models, which correspond to the traditional HMM utilized to model the human action in a particular rotation viewpoint space. In the training process, the novel model can be built by connecting the sub action models between adjacent viewpoint spaces. In the recognition process, action with unknown viewpoint is recognized by using improved forward algorithm. The proposed model can not only simplify the model training process by decomposing the parameter space into multiple sub-spaces, but also improve the performance the algorithm by constraining the possible viewpoint changing. Experiment results on IXMAS dataset demonstrated that the proposed model obtains better performance than other recent view-invariant action recognition method.     

基于多视角视频的运动重建

为了更逼真地从视频图像序列中实现三维人体骨架动画形式的提取,以便进一步地对人体运动进行分析与研究,提出了一种基于多视角视频的运动重建的方法。该方法充分利用了标记点的信息,其核心步骤有标定摄像机,提取标记点,跟踪标记点和人体运动三维重建四个主要方面。其中,在跟踪标记点时,使用了基于多视觉的目标跟踪算法,该算法由结合了扩展卡尔曼滤波预测与标记点轨迹平滑性约束所构成的双目立体视觉跟踪与多目视觉数据融合两个方面。实验结果证明了所提方法的有效性与可行性。     

Discriminant neighborhood embedding for classification

In this paper a novel subspace learning method called discriminant neighborhood embedding (DNE) is proposed for pattern classification. We suppose that multi-class data points in high-dimensional space tend to move due to local intra-class attraction or inter-class repulsion and the optimal embedding from the point of view of classification is discovered consequently. After being embedded into a low-dimensional subspace, data points in the same class form compact submanifod whereas the gaps between submanifolds corresponding to different classes become wider than before. Experiments on the UMIST and MNIST databases demonstrate the effectiveness of our method.     

An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments

A reformative kernel algorithm, which can deal with two-class problems as well as those with more than two classes, on Fisher discriminant analysis is proposed. In the novel algorithm the supposition that in feature space discriminant vector can be approximated by some linear combination of a part of training samples, called “significant nodes”, is made. If the “significant nodes” are found out, the novel algorithm on kernel Fisher discriminant analysis will be superior to the naive one in classification efficiency. In this paper, a recursive algorithm for selecting “significant nodes”, is developed in detail. Experiments show that the novel algorithm is effective and much efficient in classifying.     

基于共享隐空间的多视角SVM

传统的单视角方法对来自不同场景不同形式的多视角样本难以获得较好的分类性能,因此多视角学习成为近年来的热门研究课题并被广泛研究.在多视角学习中,可能存在这样一种特殊现象,即来自不同视角相同类的样本间的差异比来自同一视角不同类的样本间的差异大,这给多视角学习带来很大挑战,并导致多视角学习效果变差.鉴于此,首先利用Parze...