An improved SRC method based on virtual samples for face recognition

The sparse representation classifier (SRC) performs classification by evaluating which class leads to the minimum representation error. However, in real world, the number of available training samples is limited due to noise interference, training samples cannot accurately represent the test sample linearly. Therefore, in this paper, we first produce virtual samples by exploiting original training samples at the aim of increasing the number of training samples. Then, we take the intra-class difference as data representation of partial noise, and utilize the intra-class differences and training samples simultaneously to represent the test sample in a linear way according to the theory of SRC algorithm. Using weighted score level fusion, the respective representation scores of the virtual samples and the original training samples are fused together to obtain the final classification results. The experimental results on multiple face databases show that our proposed method has a very satisfactory classification performance.     

Determination of candidate subjects for better recognition of faces

In order to improve the accuracy of face recognition and to solve the problem of various poses, we present an improved collaborative representation classification (CRC) algorithm using original training samples and the corresponding mirror images. First, the mirror images are generated from the original training samples. Second, both original training samples and their mirror images are simultaneously used to represent the test sample via improved collaborative representation. Then, some classes which are “close” to the test sample are coarsely selected as candidate classes. At last, the candidate classes are used to represent the test sample again, and then the class most similar to the test sample can be determined finely. The experimental results show our proposed algorithm has more robustness than the original CRC algorithm and can effectively improve the accuracy of face recognition.     

共享空间基-逐类剩余空间基混合稀疏表示人脸识别算法

针对传统训练样本字典学习未利用类共有信息的不足,引入共享空间和与类别相关的剩余空间,提出了共享空间基-逐类剩余空间基混合稀疏表示人脸识别的算法。该算法首先提取训练样本主成分分析(PCA)特征,获取无标记的共享空间基及其重构样本得到类共有信息;然后结合原始样本得到差分训练集合,并引入类间差异信息构建逐类特异性剩余空间基;最后融合共享空间基和剩余空间基,利用残差判别函数完成模式分类。该方法不仅利用混合空间的正交特性,而且发挥剩余空间的鉴别能力和共享信息稀疏逼近的作用,使结构性字典和模式分类紧密结合。该方法的有效性,分别通过用AR、CMU PIE、Extended Yale B人脸数据库进行的实验得到验证。     

一种快速的基于稀疏表示的人脸识别算法

基于稀疏表示的人脸识别算法（SRC）识别率相当高,但是当使用l1范数求最优的稀疏表示时,大大增加了算法的计算复杂度,矩阵随着维度的增加,计算时间呈几何级别上升,该文提出利用拉格朗日算法求解矩阵的逆的推导思路,用一种简化的伪逆求解方法来代替l1范数的计算,可将运算量较高的矩阵求逆运算转变为轻量级向量矩阵运算,基于AR人脸库的实验证明,维度高的时候识别率高达97%,同时,计算复杂度和开销比SRC算法大幅度降低95%。     

Sample partition and grouped sparse representation

Recently, conventional representation-based classification (RBC) methods demonstrate promising performance in image recognition. However, conventional RBCs only use a kind of deviations between the test sample and the linear combination of training samples of each class to perform classification. In many cases, a single kind of deviations corresponding to each class cannot effectively reflect the difference between the test sample and reconstructed sample of each class. Moreover, in practical applications, limited training samples are not able to reflect the possible changes of the image sufficiently. In this paper, we propose a novel scheme to tackle the above-mentioned problems. Specifically, we first use the original training samples to generate corresponding mirror samples. Thus, the original sample set and its mirror counterpart are treated as two separate training groups. Secondly, we perform collaborative representation classification on these two groups from which each class leads to two kinds of deviations, respectively. Finally, we fuse two kinds of deviations of each class and their correlation coefficient to classify the test sample. The correlation coefficient is defined for two kinds of deviations of each class. Experimental results on four databases show the proposed scheme can improve the recognition rate in image-based recognition.     

A supervised dimensionality reduction method-based sparse representation for face recognition

The sparse representation-based classification (SRC) method is a powerful tool to present high-dimensionality data and its superiority in many fields, especially in face recognition application has been proved. With sparsity appropriately harnessed, the SRC can solve face classification problems caused by varying expression, illumination as well as occlusion and disguise. However, face images as high-dimensionality data are usually noisy and the dimensionality is always larger than the number of training sample in real-world applications, which bring a disadvantage for the performance of SRC. Therefore, it is beneficial to perform dimensionality reduction (DR) before utilizing the SRC method. But most prevalent DR methods have no direct connection to SRC. In this paper, we proposed a supervised DR algorithm which suits SRC well and improves the discriminating ability in the low-dimensionality space. The proposed method utilizes the fisher discriminant criterion and low-dimensionality reconstructive restriction to extract the discriminating structure of data. The extensive experiments on public face databases verified the effectiveness of the supervised DR with the model of sparse representation.     

Virtual images inspired consolidate collaborative representation-based classification method for face recognition

Abstract

The collaborative representation-based classification method performs well in the field of classification of high-dimensional images such as face recognition. It utilizes training samples from all classes to represent a test sample and assigns a class label to the test sample using the representation residuals. However, this method still suffers from the problem that limited number of training sample influences the classification accuracy when applied to image classification. In this paper, we propose a modified collaborative representation-based classification method (MCRC), which exploits novel virtual images and can obtain high classification accuracy. The procedure to produce virtual images is very simple but the use of them can bring surprising performance improvement. The virtual images can sufficiently denote the features of original face images in some case. Extensive experimental results doubtlessly demonstrate that the proposed method can effectively improve the classification accuracy. This is mainly attributed to the integration of the collaborative representation and the proposed feature-information dominated virtual images.     

融合2D-PCA及稀疏表示的掌纹识别方法

提出一种基于稀疏表示的掌纹识别方法,该方法借鉴二维主成分分析(PCA)良好的数据压缩属性和较快的特征提取速度,生成掌纹特征图像.二维PCA不仅克服了一维PCA数据维数过大不易计算的缺点,而且保留了原始图像的数据结构,提取的特征能更好的代表原始图像.为了便于稀疏表达,对提取的掌纹特征图像利用一维主成分分析进行二次特征提取,得到训练样本.虽然此处使用了一维PCA,但是由于这是二次特征提取,提取的特征还是保留了原始图像的数据结构,相比单纯的一维PCA,提高了识别率.利用训练样本构造出冗余字典,并采用稀疏表示理论将测试样本表示为字典原子的线性组合,然后根据表示系数的稀疏性与稀疏集中度实现分类识别.由于该方法利用了表达系数的稀疏性,因此减小了算法的时间和空间复杂度.实验表明,针对香港理工大学的MSpalmprints Database,本文方法的识别率较传统方法有明显提高.     

Face recognition based on symmetrical virtual image and original training image

In face representation-based classification methods, we are able to obtain high recognition rate if a face has enough available training samples. However, in practical applications, we only have limited training samples to use. In order to obtain enough training samples, many methods simultaneously use the original training samples and corresponding virtual samples to strengthen the ability of representing the test sample. One is directly using the original training samples and corresponding mirror samples to recognize the test sample. However, when the test sample is nearly symmetrical while the original training samples are not, the integration of the original training and mirror samples might not well represent the test samples. To tackle the above-mentioned problem, in this paper, we propose a novel method to obtain a kind of virtual samples which are generated by averaging the original training samples and corresponding mirror samples. Then, the original training samples and the virtual samples are integrated to recognize the test sample. Experimental results on five face databases show that the proposed method is able to partly overcome the challenges of the various poses, facial expressions and illuminations of original face image.     

Weighted Sparse Image Classification Based on Low Rank Representation

The conventional sparse representation-based image classification usually codes the samples independently, which will ignore the correlation information existed in the data. Hence, if we can explore the correlation information hidden in the data, the classification result will be improved significantly. To this end, in this paper, a novel weighted supervised spare coding method is proposed to address the image classification problem. The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation. And then, it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in a supervised way. Experimental results show that the proposed method is superiority to many conventional image classification methods.     

Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for vehicle classification. In our proposed method, the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients (MFCC). Moreover, we extend our model to handle sparse environmental noise. We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.     

Human Action Recognition Based on Supervised Class-Specific Dictionary Learning with Deep Convolutional Neural Network Features

Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining one modified sparse classification model and deep convolutional neural network (CNN) features. Secondly, we construct a novel classification model which consists of the representation-constrained term and the coefficients incoherence term. Experimental results on benchmark datasets show that our modified model can obtain competitive results in comparison to other state-of-the-art models.     

基于核竞争学习算法的图像特征提取

利用核函数学习可有效解决图像特征线性不可分的特性,结合稀疏表示算法的优势,提出了一种新的图像特征提取方法。采用基于竞争学习规则的独立分量分析法对图像进行稀疏表示,该算法可提取数据的高维特征,且不需要优化高阶的非线性函数和进行稀疏密度估计,因而有较快的收敛速度。与仅使用基于竞争学习的独立分量分析法相比,在PolyU数据库上的实验结果表明,采用基于核函数学习和稀疏表示相结合的方法所提取的数据特征有利于提高特征分类精度。     

基于自相似性与稀疏表示的超分辨率算法

目的为了解决当前稀疏表示的超分辨率算法效果依赖参与训练的数据的问题,结合图像的自相似性,提出一种基于自相似性与稀疏表示相结合的超分辨率算法。方法算法利用图像的多维自相似性,构建多维图像金字塔,采用改进的相似块搜索策略,得到对应的高低分辨率图像块作为训练样本,然后对样本进行字典训练,最后根据稀疏表示得到超分辨率图像。结果实验结果显示,文中算法在峰值信噪比(PSNR)和结构相似度(SSIM)上优于其他算法,对于实验图像而言,PSNR平均提升了0.5 dB。结论提出的超分辨率算法未引入外部数据库,具有较好的效果,能够用于超分辨率重建。     

应用一种多核稀疏表示模型实现掌纹分类

与稀疏表示(SR)模型相比,基于单个核函数的SR(KSR)模型可以有效减少数据维数、降低学习模型的计算复杂度并提高特征分类精度;但这种模型对核函数及其参数的选择通常不能包含恰当的、完整的分类信息。为了满足更高的特征分类精度需求,提出了一种基于多个核函数的SR(M-KSR)模型及其快速稀疏优化方法,并将其应用于掌纹图像的分类。测试结果证明了基于M-KSR模型的掌纹分类方法的有效性和实用性。     

基于字典学习的主动声呐目标分类方法

主动声呐目标分类在军事和民用方面都有重要的应用和价值。文章基于稀疏表示理论,结合K-奇异值分解和正交匹配追踪算法,提出一种基于学习字典的稀疏表示分类方法（Dictionary Learning Sparse Representation Classification,DLSRC）。首先,利用K-奇异值分解算法训练各个类别目标回波信号,得到带有目标特征信息的类别字典,类别字典对信号具有良好表征能力并且带有目标类别信息;然后,利用正交匹配追踪算法和各个类别字典稀疏分解测试信号,得到各个类别字典下的稀疏系数后重构信号;最后,根据各个重构信号与测试信号的匹配度判定类别,得到分类准确率。结果显示,200个测试数据在信噪比分别为-5、-3、6 dB时,DLSRC法的分类准确率分别达到87%、89%、95.5%。不同信噪比下基于学习字典稀疏表示分类方法的准确率均高于已有的支持向量机（Support Vector Machine,SVM）、K-最近邻（K-Nearest Neighbor,KNN）和柔性最大值分类器（SoftMax）等分类方法,具有较好的分类性能。     

基于改进判别字典学习的故障诊断方法

近年来,基于稀疏表示的分类技术在模式识别中取得一定的成功。该框架中,字典的学习和分类器的训练通常是两个独立的模块,降低了方法的识别精度。针对以上问题,提出了一种特征提取和模式识别相融合的改进判别字典学习模型,将重构误差项、稀疏编码判别项及分类误差项进行了整合,并用K奇异值分解算法对目标函数进行优化,实现了字典和分类器的同步学习。该方法先对原始信号进行经验模态分解,并从分解的本征模态函数中提取时、频特征,形成故障样本;然后将训练样本输入改进模型用K奇异值分解优化;最后用习得字典及分类器权重对测试样本进行识别。实验结果表明：该算法不但适用于小样本故障问题,而且鲁棒性和分类性能都明显高于其它算法。         

基于核映射稀疏表示分类的轴承故障诊断

针对传统稀疏表示分类算法在低维空间分类精度难以保证问题,论文提出了基于核映射的稀疏表示分类算法。采用核映射方法获得了低维样本在高维空间的坐标,改善了样本间的线性可分度;在此基础上,利用稀疏表示分类算法获得样本在高维空间上的稀疏解。经滚动轴承故障分类实验验证：新算法对核参数具有较高的鲁棒性;可明显提高分类精度。     

Classification of multicolor fluorescence in situ hybridization (M-FISH) images with sparse representation

There has been a considerable interest in sparse representation and compressive sensing in applied mathematics and signal processing in recent years but with limited success to medical image processing. In this paper we developed a sparse representation-based classification (SRC) algorithm based on L1-norm minimization for classifying chromosomes from multicolor fluorescence in situ hybridization (M-FISH) images. The algorithm has been tested on a comprehensive M-FISH database that we established, demonstrating improved performance in classification. When compared with other pixel-wise M-FISH image classifiers such as fuzzy c-means (FCM) clustering algorithms and adaptive fuzzy c-means (AFCM) clustering algorithms that we proposed earlier the current method gave the lowest classification error. In order to evaluate the performance of different SRC for M-FISH imaging analysis, three different sparse representation methods, namely, Homotopy method, Orthogonal Matching Pursuit (OMP), and Least Angle Regression (LARS), were tested and compared. Results from our statistical analysis have shown that Homotopy based method is significantly better than the other two methods. Our work indicates that sparse representations based classifiers with proper models can outperform many existing classifiers for M-FISH classification including those that we proposed before, which can significantly improve the multicolor imaging system for chromosome analysis in cancer and genetic disease diagnosis.