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。     

一种基于自适应核字典学习的SAR目标识别方法

提出一种基于自适应核字典学习的合成孔径雷达（synthetic aperture radar，SAR）目标识别方法.该方法首先将SAR图像的特征信息通过核函数映射到高维度的核空间中并进行字典学习；然后根据更新后的字典动态计算稀疏度；最后依据最小重构误差准则实现SAR目标识别.在公开数据集MSTAR上的仿真实验结果表明，该方法提取到的特征信息可分度高，对SAR目标的识别具有较好的性能.     

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.     

基于代理函数优化的稀疏性字典学习

稀疏性字典学习是指对在某个已知的基字典上具有稀疏表示的字典的学习.论文利用块松弛思想,将稀疏性字典学习问题转化为字典和系数的分别优化问题,利用代理函数优化方法分别对固定字典和固定系数情况下的目标函数进行优化处理,得到固定字典情况下的系数更新算法和固定系数情况下的字典更新算法,进而得到稀疏性字典学习算法.理论分析说明了本...     

同伦方法在图像稀疏去噪中的应用

本文在深入研究稀疏表示和字典学习理论的基础上,建立了图像去噪模型并提出一种新的图像去噪算法。该算法采用同伦方法学习字典,充分利用了同伦方法收敛速度快以及对信号的恢复准确度高的特点。之后利用 OMP 算法求出带噪图像在该字典下的稀疏表示系数,并结合稀疏去噪模型实现对图像的去噪。实验结果显示本文算法在不同的噪声环境下具有较好的去噪效果,同时在与 K-SVD 算法关于收敛速度比较的实验中,实验结果充分显示了使用同伦算法学习字典在收敛速度上的优势。      

The nonparametric Bayesian dictionary learning based interpolation method for WSNs missing data

The conventional data interpolation methods based on sparse representation usually assume that the signal is sparse under the overcomplete dictionary. Specially, they must confirm the dimensions of dictionary and the signal sparse level in advance. However, it is hard to know them if the signal is complicated or dynamically changing. In this paper, we proposed a nonparametric Bayesian dictionary learning based interpolation method for WSNs missing data, which is the combination of sparse representation and data interpolation. This method need not preset sparse degrees and dictionary dimensions, and our dictionary atoms are drawn from a multivariate normal distribution. In this case, the dictionary size will be learned adaptively by the nonparametric Bayesian method. In addition, we implement the Dirichlet process to exploit the spatial similarity of the sensing data in WSNs, thus to improve the interpolation accuracy. The interpolation model parameters, the optimal dictionary and sparse coefficients, can be inferred by the means of Gibbs sampling. The missing data will be estimated commendably through the derived parameters. The experimental results show that the data interpolation method we proposed outperforms the conventional methods in terms of interpolation accuracy and robustness.     

基于多核稀疏编码的三维人体姿态估计

为了准确有效的重构多视角图像中的三维人体姿态,该文提出一种基于多核稀疏编码的人体姿态估计算法.首先,针对连续帧姿态估计的歧义问题,该文设计了一种用于表达多视角图像的HA-SIFT描述子,其中,人体局部拓扑、肢体相对位置及外观信息被同时编码;然后,在多核学习框架下建立同时考虑特征空间内在流形结构与姿态空间几何信息的目标函数,并在希尔伯特空间优化目标函数以更新稀疏编码、过完备字典与多核权值;最后,利用姿态字典原子的线性组合来估计对应未知输入的三维人体姿态.实验结果表明,与核稀疏编码、Laplace稀疏编码及Bayesian稀疏编码相比,文本方法具有更高的估计精度.     

基于稀疏表示及光谱信息的高光谱遥感图像分类

该文结合稀疏表示及光谱信息提出了一种新的高光谱遥感图像分类算法。首先提出利用高光谱遥感图像数据集构造学习字典,然后根据学习字典计算每个像元的稀疏系数,从而获得像元的稀疏表示特征,最后根据稀疏表示特征和光谱信息分别构造随机森林,通过投票机制得到最终的分类结果。在AVIRIS高光谱遥感图像上的实验结果表明:该文所提方法能够提高分类效果,且其分类总精度和Kappa系数要高于光谱信息和稀疏表示特征方法。     

Learning Sparse Overcomplete Codes for Images

Images can be coded accurately using a sparse set of vectors from a learned overcomplete dictionary, with potential applications in image compression and feature selection for pattern recognition. We present a survey of algorithms that perform dictionary learning and sparse coding and make three contributions. First, we compare our overcomplete dictionary learning algorithm (FOCUSS-CNDL) with overcomplete independent component analysis (ICA). Second, noting that once a dictionary has been learned in a given domain the problem becomes one of choosing the vectors to form an accurate, sparse representation, we compare a recently developed algorithm (sparse Bayesian learning with adjustable variance Gaussians, SBL-AVG) to well known methods of subset selection: matching pursuit and FOCUSS. Third, noting that in some cases it may be necessary to find a non-negative sparse coding, we present a modified version of the FOCUSS algorithm that can find such non-negative codings. Efficient parallel implementations in VLSI could make these algorithms more practical for many applications.     

Single-Pass K-SVD for Efficient Dictionary Learning

Sparse representation has been widely used in machine learning, signal processing and communications. K-SVD, which generalizes k-means clustering, is one of the most famous algorithms for sparse representation and dictionary learning. K-SVD is an iterative method that alternates between encoding the data sparsely by using the current dictionary and updating the dictionary based on the sparsely represented data. In this paper, we introduce a single-pass K-SVD method. In this method, the previous input data are first summarized as a condensed representation of weighted samples. Then, we developed a weighted K-SVD algorithm to learn a dictionary from the union of this representation and the newly input data. Experimental results show that our approach can approximate K-SVD’s performance well by consuming considerably less storage resource.     

$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity in this field has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. Both of these techniques have been considered, but this topic is largely still open. In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method—the$ K$-SVD algorithm—generalizing the$ K$-means clustering process.$ K$-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. The update of the dictionary columns is combined with an update of the sparse representations, thereby accelerating convergence. The$ K$-SVD algorithm is flexible and can work with any pursuit method (e.g., basis pursuit, FOCUSS, or matching pursuit). We analyze this algorithm and demonstrate its results both on synthetic tests and in applications on real image data.     

基于空间角稀疏表示的二维DOA估计

论文提出一种基于空间角稀疏表示的维DOA估计方法,解决了维DOA估计中冗余字典构造的难题。构建的空间角包含了方位角和俯仰角的信息,利用其构造冗余字典可以将方位角和俯仰角的组合从维空间映射到1 维空间,极大地降低了字典的长度和求解的复杂度;同时将算法推广到频域,扩展了其应用范围。与传统的高分辨算法相比,该方法对信噪比和快拍数要求不高、无需特征值分解和多维搜索过程。理论分析和仿真实验,验证了该方法对维相干信号和非相干信号都具有较高的估计精度和较好的分辨力,在不同信噪比下性能优于MUSIC算法。     

基于自适应冗余字典的语音信号稀疏表示算法

基于冗余字典的信号稀疏表示是一种新的信号表示理论,当前的理论研究主要集中在字典构造算法和稀疏分解算法两方面。该文提出一种新的基于自适应冗余字典的语音信号稀疏表示算法,该算法针对自相关函数为指数衰减的平稳信号,从K-L展开出发,建立了匹配信号结构的冗余字典,进而提出一种高效的基于非线性逼近的信号稀疏表示算法。实验结果表明冗余字典中原子的自适应性和代数结构使短时平稳语音信号稀疏表示具有较高的稀疏度和较好的重构精度,并使稀疏表示算法较好地应用于语音压缩感知理论。     

一种多算法融合的人脸识别方法研究

针对人脸识别技术易受光照、姿态、表情等影响 ,为了增强人脸识别算法的鲁棒性,提出首先采用 LBP算法提取人脸图像的局部纹理特征,使用PCA算法将高维的空间人脸图像投影到低维的 特征空间,使 用LDA算法利用人脸类别标签信息寻找最优的投影向量,实现了人脸图像维度进一步地压缩 ,最后使用SVM 分类器分类匹配得到识别结果。分别使用ORL和Yale人脸数据库验证了算法的有效性,实 验结果表明,文 中该方法具有良好的识别性能,与其它的识别算法相比,识别率有了较大的提高。     

Incoherent dictionary learning and sparse representation for single-image rain removal

The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.     

基于小波字典稀疏表示的SAR图像目标识别

针对稀疏表示识别算法在图像域构造冗余字典时过分依赖预处理及原子维数较大的问题,提出基于小波字典的 SAR图像稀疏表示识别算法。首先采用二维离散小波变换将原始图像变换到小波域,建立小波域 SAR图像特征模型,得出小波域低频成分可充分表征目标类别信息的结论。然后取小波域低频成分进行2DPCA特征抽取构造小波字典,最后由改进 OMP 算法稀疏分解系数得到识别结果。SAR MSTAR数据的实验结果表明,在无预处理的情况下识别率高达99％,并且在含噪比10％的情况下识别率仍达96％。     

Atom specific multiple kernel dictionary based Sparse Representation Classifier for medium scale image classification

Kernel based Sparse Representation Classifier (KSRC) can classify images with acceptable performance. In addition, Multiple Kernel Learning based SRC (MKL-SRC) computes the weighted sum of multiple kernels in order to construct a unified kernel while the weight of each kernel is calculated as a fixed value in the training phase. In this paper, an MKL-SRC with non-fixed kernel weights for dictionary atoms is proposed. Kernel weights are embedded as new variables to the main KSRC goal function and the resulted optimization problem is solved to find the sparse coefficients and kernel weights simultaneously. As a result, an atom specific multiple kernel dictionary is computed in the training phase which is used by SRC to classify test images. Also, it is proved that the resulting optimization problem is convex and is solvable via common algorithms. The experimental results demonstrate the effectiveness of the proposed approach.     

特征聚类的局部敏感稀疏图像修复

针对图像修复过程中单一的字典迭代时间长、适应性差、修复效果不理想的缺点,提出了一种结合图像特征聚类与字典学习的改进的图像修复方式。首先破损的图像被分割成小块,并产生索引矩阵。然后使用控制核回归权值算法,对其进行图像聚类。通过对图像内在结构与未破损区域信息的挖掘,分割的图像块根据SKRW的相似性进行了分类。之后针对不同类型结构的图像,通过自适应局部明感字典学习的方式,获取每类字典的过完备字典。然后,通过构建自适应局部配适器,提高字典更新的收敛速度与稀疏字典的适应性。因为是通过多个字典匹配不同结构的图像,因此图像的稀疏表示更为准确。各个字典在达到收敛之前不断进行更新,而图像的稀疏因子也会随着改变。在对破损区域进行补丁更换之后,实现了对破损图像的修复。实验结果表明,该算法相较于目前的修复算法,视觉效果和客观评价上更好,且所需的修复时间更短。     

基于稀疏编码和集成学习的多示例多标记图像分类方法

 该文基于稀疏编码和集成学习提出了一种新的多示例多标记图像分类方法。首先,利用训练包中所有示例学习一个字典,根据该字典计算示例的稀疏编码系数;然后基于每个包中所有示例的稀疏编码系数计算包特征向量,从而将多示例多标记问题转化为多标记问题;最后利用多标记分类算法进行求解。为了提高分类器的泛化能力,对多个分类器进行集成。在多示例多标记图像数据集上的实验结果表明所提方法与其它方法相比有更好的性能。     

熵权约束稀疏表示的短文本分类算法

针对短文本特征稀疏性问题,提出一种熵权约束稀疏表示的短文本分类方法.考虑到初始字典维数较高,首先,利用Word2vec工具将字典中的词表示成词向量形式,然后根据加权向量平均值对原始字典进行降维.其次,利用一种快速特征子集选择算法去除字典中不相关和冗余短文本,得到过滤后的字典.再次,基于稀疏表示理论在过滤后的字典上,为目标函数设计一种熵权约束的稀疏表示方法,引入拉格朗日乘数法求得目标函数的最优值,从而得到每个类的子空间.最后,在学习到的子空间下通过计算待分类短文本与每个类中短文本的距离,并根据三种分类规则对短文本进行分类.在真实数据集上的大量实验结果表明,本文提出的方法能够有效缓解短文本特征稀疏问题且优于现有短文本分类方法.