Novel classification rule of two-phase test sample sparse representation

In this paper, a novel classifier based on two-phase test sample sparse representation (TPTSSR) classifier and coarse k nearest neighbor (C-kNN) classifier, called novel classification rule of two-phase test sample sparse representation (NCR-TPTSSR) classifier, is proposed for image recognition. Being similar to TPTSSR classifier and C-kNN classifier, NCR-TPTSSR classifier also uses the two phases to classify the test sample. However, the classification rule of NCR-TPTSSR classifier is different to the decision rule of TPTSSR classifier and C-kNN classifier. A large number of experiments on FERET face database, AR face database, JAFFE face database and PolyU FKP database are used to evaluate the proposed algorithm. The experimental results demonstrate that the proposed method achieves better recognition rate than TPTSSR classifier, C-kNN classifier, nearest feature center (NFC) classifier, nearest feature line (NFL) classifier, nearest neighbor (NN) and so on.     

A sparse representation method based on kernel and virtual samples for face recognition

To improve the classification accuracy of face recognition, a sparse representation method based on kernel and virtual samples is proposed in this paper. The proposed method has the following basic idea: first, it extends the training samples by copying the left side of the original training samples to the right side to form virtual training samples. Then the virtual training samples and the original training samples make up a new training set and we use a kernel-induced distance to determine M nearest neighbors of the test sample from the new training set. Second, it expresses the test sample as a linear combination of the selected M nearest training samples and finally exploits the determined linear combination to perform classification of the test sample. A large number of face recognition experiments on different face databases illustrate that the error ratios obtained by our method are always lower more or less than face recognition methods including the method mentioned in Xu and Zhu [21], the method proposed in Xu and Zhu [39], sparse representation method based on virtual samples (SRMVS), collaborative representation based classification with regularized least square (CRC_RLS), two-phase test sample sparse representation (TPTSSR), and the feature space-based representation method.     

Face image recognition via collaborative representation on selected training samples

In this paper, we present a collaborative representation-based classification on selected training samples (CRC_STS) for face image recognition. The CRC_STS uses a two stage scheme: The first stage is to select some most significant training samples from the original training set by using a multiple round of refining process. The second stage is to use collaborative representation classifier to perform classification on the selected training samples. Our method can be regarded as a sparse representation approach but without imposing l₁-norm constraint on representation coefficients. The experimental results on three well known face databases show that our method works very well.     

Modular Fisher Discriminant Sparse Representation for robust face recognition

This paper proposes a novel framework for robust face recognition based on sparse representation and discrimination ranking. This method consists of three stages. The first stage partitions each training sample into some overlapped modules and then computes each module's Fisher ratio, respectively. The second stage selects modules which have higher Fisher ratios to comprise a template to filter training and test images. The dictionary is constructed by the filtered training images. The third stage computes the sparse representation of filtered test sample on the dictionary to perform identification. The advantages of the proposed method are listed as follows: the first stage can preserve the local structure. The second stage removes the modules that have little contribution for classification. Then the method uses the retaining modules to classify the test sample by SRC which makes the method robust. Compared with the related methods, experimental results on benchmark face databases verify the advancement of the proposed method. The proposed method not only has a high accuracy but also can be clearly interpreted.     

基于深度残差网络的脱机手写汉字识别研究

摘 要: 手写汉字识别是模式识别与机器学习的重要研究方向和应用领域。近年来,随着深度学习理论方法的完善、新技术的层出不穷,深度神经网络在图像识别分类、图像生成等典型应用中取得了突破性的进展,其中,深度残差网络作为最新的研究成果,已成功应用于手写数字识别、图片识别分类等多个领域。本文将研究深度残差网络在脱机孤立手写汉字识别中的应用方法,通过改进残差学习模块的单元结构,优化深度残差网络性能,同时通过对训练集的预处理,从数据层面实现训练生成模型性能的提升,最后设计实验,验证深度残差网络、End-to-End模式在脱机手写汉字识别中的可行性,分析、总结存在的问题及今后的研究方向。     

A two-phase face recognition method in frequency domain

In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images.     

联合因子分析和稀疏表示在稳健性说话人确认中的应用

在说话人确认的任务中,为了解决信道失配问题,提高系统性能,引入了联合因子分析和稀疏表示算法。首先利用联合因子分析算法去除信道干扰,得到与信道无关的说话人因子,然后在稀疏表示算法中利用说话人因子构建过完备字典,求解稀疏最优化问题计算说话人得分。由于此方法有机结合了联合因子分析算法的信道鲁棒性和稀疏表示的鉴别性,使用此算法构建的系统在NIST SRE 2008电话训练、电话测试数据集上性能表现良好,相对于联合因子分析-支持向量机系统在性能上有竞争性,在原理上有互异性,系统融合更带来了最小检测代价指标上4.91%的性能提升。实验表明使用联合因子分析与稀疏表示进行说话人确认是可行的。     

冲击声的稀疏特征提取及声源类型识别

针对低信噪比下声源材料类型的细分任务,将稀疏表达用于冲击声信号的声源类型识别,提取的稀疏特征相比传统的MFCC特征有效改善了识别性能。分别基于3种预定义词典和一组根据训练信号学习的词典,利用正交匹配追踪(OMP)方法对录制冲击声进行稀疏表达,提取稀疏特征用于不同信噪比下冲击声信号的声源辨识,并与MFCC特征进行比较。对包含12类材料的冲击声数据库的分类结果显示,在几乎所有情况下,稀疏特征比MFCC特征具有更好的识别效果。特别是在信噪比较低的情况下,稀疏特征具有更好的抗噪性能。     

基于可见-近红外光谱与稀疏表示的注水肉识别

为了能快速准确的识别原料肉与注水肉,提出了一种基于可见-近红外光谱和稀疏表示的无损的识别方法。通过向猪肉样本(包括猪皮、脂肪层和肌肉层)注水的方法建立注水肉模型,采集未注水的原料肉和6类不同注水量的注水肉的可见和近红外漫反射光谱数据。为了消除光谱数据中的冗余信息并提高分类效果,对光谱数据进行光调制和归一化等预处理并截取有效波段,根据是否注水以及注水量的多少对样本进行分类。用所有训练样本构成原子库(字典),通过ｌ1最小化将测试样本表示为这些原子的最稀疏的线性组合。计算测试样本与各类的投影误差,将最小投影误差对应的类作为测试样本的所属类别,并应用留一法进行交叉检验,比较了稀疏表示法与支持向量机的识别结果。实验结果表明,利用稀疏表示法对于原料肉与注水肉的识别准确率可达到90%以上,获得了较好的分类效果,优于支持向量机的识别结果。而对于不同注水量的注水肉识别准确率与注水量之差正相关。稀疏方法不需要进行传统模式识别模型的前期学习与特征提取,适用于高维、小样本量数据的处理,计算成本低,将其用于注水肉的光谱数据识别具有一定的创新性,并取得了较满意的结果,为原料肉和注水肉的无损识别提供了一种有效方法。     

A face recognition algorithm based on collaborative representation

In this paper, we propose a face recognition algorithm by incorporating a neighbor matrix into the objective function of sparse coding. We first calculate the neighbor matrix between the test sample and each training sample by using the revised reconstruction error of each class. Specifically, the revised reconstruction error (RRE) of each class is the division of the l₂-norm of reconstruction error to the l₂-norm of reconstruction coefficients, which can be used to increase the discrimination information for classification. Then we use the neighbor matrix and all the training samples to linearly represent the test sample. Thus, our algorithm can preserve locality and similarity information of sparse coding. The experimental results show that our algorithm achieves better performance than four previous algorithms on three face databases.     

基于稀疏表示模型和自回归模型的高光谱分类

针对高光谱分类中对光谱信息和空间信息利用不足的问题,提出了一种基于稀疏表示模型和自回归模型相结合的分类算法。该算法利用稀疏表示模型和自回归模型,设计联合字典:在光谱维上,利用稀疏表示模型将高光谱的每个光谱向量表示为字典中训练样本的稀疏线性组合;在空间维上,利用自回归模型对每个光谱向量的8邻域进行约束。针对不同样本分别构造一个字典,在减少计算量的同时减小重构误差,最后在最小重构误差和邻域相关性的约束下求解稀疏表示问题,以最小重构误差为准则实现高光谱数据的分类。仿真结果表明,该方法能够有效地提高高光谱数据的分类精度。     

Concentrative sparse representation based classification

Sparse representation is being proved to be effective for many tasks in the field of pattern recognition. In this paper, an efficient classification algorithm based on concentrative sparse representation will be proposed to address the problem caused by insufficient training samples in each class. We firstly compute representation coefficient of the testing sample with training samples matrix using subspace pursuit recovery algorithm. Then we define concentration measurement function in order to determine whether the sparse representation coefficient is concentrative. Subspace pursuit is repeatedly used to revise the sparse representation until concentration is met. Such a concentrative sparse representation can contribute to discriminative residuals that are critical to accurate classification. The experimental results have showed that the proposed algorithm achieves a satisfying performance in both accuracy and efficiency.     

基于稀疏表示的核素能谱特征提取及核素识别

提出了一种基于稀疏表示的核素能谱特征提取方法,其实质是将核素能谱在区分性最好的稀疏原子上进行投影。利用稀疏分解方法对核素能谱进行稀疏分解,提取分解系数向量作为表征核素的特征向量,通过模式识别分类方法建立分类模型实现核素识别。与传统稀疏分解方法的区别在于：在能谱稀疏分解过程中按照稀疏字典中的原子排列顺序顺次进行分解;其次,分解目的在于特征提取,即最终提取到的特征对不同核素具有可区分性,并不要求核素能谱的重构精度。在²⁴¹Am, ¹³³Ba, ⁶⁰Co, ¹³⁷Cs, ¹³¹I和¹⁵²Eu共6种核素1200个能谱数据上进行了核素识别实验,7种不同分类算法的平均识别率达到91.71%,实验结果的统计分析表明,本文提出的特征提取方法识别准确率显著地高于两种传统核素能谱特征提取方法准确率。     

A novel method for recognizing face with partial occlusion via sparse representation

In this paper, we propose a novel method to recognize the partially occluded face images based on sparse representation. Generally, occlusions, such as glasses and scarf, fall on some random patch of image's pixels of test images, but which is known to be connected. In our method, all images are divided into several blocks and then an indicator based on linear regression technique is presented to determine whether a block is occluded. We utilize those uncontaminated blocks as the new feature of an image. Finally, the sparse representation-based classification (SRC) method serves as the classifier to recognize unknown faces. In the original work of SRC, the extended SRC (eSRC) scheme is presented to deal with occlusions, which has very heavy computational cost. The experimental results show that our method can achieve good recognition accuracy and has much lower computational cost than eSRC.     

Using the nonlinear dimensionality reduction method for the prediction of subcellular localization of Gram-negative bacterial proteins

One of the central problems in computational biology is protein function identification in an automated fashion. A key step to achieve this is predicting to which subcellular location the protein belongs, since protein localization correlates closely with its function. A wide variety of methods for protein subcellular localization prediction have been proposed over recent years. Linear dimensionality reduction (DR) methods have been introduced to address the high-dimensionality problem by transforming the representation of protein sequences. However, this approach is not suitable for some complex biological systems that have nonlinear characteristics. Herein, we use nonlinear DR methods such as the kernel DR method to capture the nonlinear characteristics of a high-dimensional space. Then, the K-nearest-neighbor (K-NN) classifier is employed to identify the subcellular localization of Gram-negative bacterial proteins based on their reduced low-dimensional features. Experimental results thus obtained are quite encouraging, indicating that the applied nonlinear DR method is effective to deal with this complicated problem of predicting subcellular localization of Gram-negative bacterial proteins. An online web server for predicting subcellular location of Gram-negative bacterial proteins is available at .     

Classifier ensembles for fMRI data analysis: an experiment

Functional magnetic resonance imaging (fMRI) is becoming a forefront brain–computer interface tool. To decipher brain patterns, fast, accurate and reliable classifier methods are needed. The support vector machine (SVM) classifier has been traditionally used. Here we argue that state-of-the-art methods from pattern recognition and machine learning, such as classifier ensembles, offer more accurate classification. This study compares 18 classification methods on a publicly available real data set due to Haxby et al. [Science 293 (2001) 2425–2430]. The data comes from a single-subject experiment, organized in 10 runs where eight classes of stimuli were presented in each run. The comparisons were carried out on voxel subsets of different sizes, selected through seven popular voxel selection methods. We found that, while SVM was robust, accurate and scalable, some classifier ensemble methods demonstrated significantly better performance. The best classifiers were found to be the random subspace ensemble of SVM classifiers, rotation forest and ensembles with random linear and random spherical oracle.     

联合稀疏表示在水下目标分类中的应用

水下目标分类识别的性能受所选特征的限制,多特征往往可以获得更加稳定的结果,针对这一问题,提出了一种基于联合稀疏表示模型的水下目标分类识别方法。首先对水下目标回波信号提取3种具有信息互补性与关联性的特征:中心矩特征、小波包能量谱特征、梅尔频率倒谱系数特征,然后应用加速近端梯度法对联合稀疏表示模型进行优化,求解得到最优联合稀疏系数,最后根据最小误差准则确定目标类别。在消声水池开展模拟实验,对6类目标进行分类识别,结果表明:与传统算法相比,提出的算法具有更高识别准确率,并且其执行效率较传统算法有很大提升。     

基于稀疏表示和学习图正则的高光谱图像特征提取

针对传统局部特征提取算法难以确定邻域参数,以及仅考虑数据间的单一结构而漏掉重要信息的问题,提出一种基于稀疏表示和学习图正则的局部判别与全局稀疏保持投影算法。该算法首先对稀疏表示模型施加基于学习的图正则器,用该改进的稀疏表示模型自适应揭示样本数据间的局部线性结构,通过局部判别模型全局集成算法来提取局部线性结构中的判别信息;利用基于学习图正则稀疏表示模型构建的新型稀疏图来揭示数据间的全局稀疏结构;使得数据的局部判别结构和全局稀疏结构在低维特征空间得以保持。通过1-近邻和支持向量机分类器对实验结果进行评估,在PaviaU和Indian Pines两个高光谱公共数据集上的实验显示,提出的局部判别与全局稀疏保持投影算法较对比算法取得了最好的性能,由于提取了全局和局部的判别信息,有效提升了高光谱图像的地物分类精度。