检索结果-内蒙古大学图书馆

Nonparametric kernel sparse representation-based classifier

PATTERN RECOGNITION LETTERS 2017年第Apr.1期89卷 46-52页

作者： Esmaeilzehi, Alireza Moghaddam, Hamid Abrishami KN Toosi Univ Technol Fac Elect Engn Tehran Iran Univ Picardie Jules Verne Fac Med GRAMFC Unite Genie Biophys & Med Amiens France

sparse representation-based classifier (SRC) and kernel sparse representation-based classifier (KSRC) are founded on combining pattern recognition and compressive sensing methods and provide acceptable results in many machine learning problems. Nevertheless, these classifiers suffer from some shortcomings. For instance, SRC's accuracy drops against samples from same directional classes or KSRC's output declines when data is not normally distributed in kernel space. This paper introduces nonparametric kernel sparse representation-based classifier (NKSRC) as a generalized framework for SRC and KSRC. First, it applies kernel on samples to overcome data directionality and then employs nonparametric discriminant analysis (NDA) to reduce data dimensionality in kernel space alleviating concern about data distribution type. The experimental results of NKSRC demonstrate its superiority over SRC and KSRC-LDA and its equal or superior performance with respect to KSRC-PCA on some synthetic, four well-known face recognition and several UCI datasets. (C) 2017 Published by Elsevier B.V.

关键词： sparse representation-based classifier Compressive sensing Kernel method Nonparametric discriminant analysis Face recognition

来源：评论

学校读者我要写书评

暂无评论

Dental hard tissue morphological segmentation with sparse representation-based classifier

引用

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2019年第8期57卷 1629-1643页

作者： Cheng, Bin Wang, Wei Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn 580 Jungong Rd Shanghai 200093 Peoples R China

In the field of dental image processing and analysis, automatic segmentation results of dental hard tissue can provide a useful reference for the clinical diagnosis and treatment process. However, the segmentation accuracy is greatly affected due to the limitation of imaging conditions in the oral environment, as well as the complexity of dental hard tissue topology. To further improve the precision of dental hard tissue segmentation, a novel algorithm was presented by using the sparse representation-based classifier and mathematical morphology operations. First, the captured dental image was preprocessed to eliminate the impact of imbalance local illumination. Then, the preliminary dental hard tissue areas were calculated as the initial marker regions based on color characteristics analysis, and the sparse representation-based classifier was applied sequentially to optimize the initial marker regions combined with certain morphological operations. Finally, a modified marker-controlled watershed transform was employed to segment dental hard tissue regions on the basis of the optimized marker regions, and the final results were obtained after homogeneous region merging. The experimental results show that our method has better adaptability and robustness than existing state-of-the-art methods.

关键词： Dental image segmentation sparse representation-based classifier Watershed transformation Morphological operations

来源：评论

学校读者我要写书评

暂无评论

Kernel sparse representation-based classifier ensemble for face recognition

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2015年第1期74卷 123-137页

作者： Zhang, Li Zhou, Wei-Da Li, Fan-Zhang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China AI Speech Ltd Suzhou 215123 Peoples R China

Kernel sparse representation-based classifier (KSRC) has been proposed, which has good representation and classification performance on face image data. The performance of KSRC on face image data is partly dependent on the random projection matrix when using the random projection method and the kernel Gram matrix. This paper develops the kernel sparse representation-based classifier ensemble (KSRCE), which does not require to consider the effect of random projection and kernel Gram matrix on KSRC. Actually, the random projection matrix and the kernel Gram matrix could be used for designing the diversity schemes for KSRCE. In the combination stage, we can combine the labels or the reconstruction errors of a test sample. Experimental results on three face data sets show that KSRCE is very promising.

关键词： sparse representation-based classifier Kernel method Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Proposals for local basis selection for the sparse representation-based classifier

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2018年第8期12卷 1595-1601页

作者： Dornaika, F. El Traboulsi, Y. Univ Basque Country UPV EHU San Sebastian Spain Ikerbasque Basque Fdn Sci Bilbao Spain Lebanese Int Univ Tripoli Lebanon Lebanese Univ LaMA Tripoli Lebanon

In the domains of pattern recognition and computer vision, sparse representation classifier and its variants are considered as powerful classifiers. However, due to the use of sparse coding in most of its variants, classifying test samples is computationally expensive. Thus, it is not practical for scenarios demanding fast classification. For this reason, a two-phase coding classifier based on classic regularized least square was proposed recently. A significant limitation of this classifier is the fact that the number of local bases that should be handed over to the next coding phase should be specified manually. This paper overcomes this main limitation and proposes three data-driven schemes allowing an automatic estimation of the optimal size of the local bases. Experiments conducted on five image datasets show that the introduced schemes, despite their simplicity, can improve the performance of the two-phase linear coding classifier adopting ad hoc choices for the number of local bases.

关键词： sparse coding sparse representation-based classifier Parameter selection Self-tuning Image classification

来源：评论

学校读者我要写书评

暂无评论

Critical parameters of the sparse representation-based classifier

引用

IET COMPUTER VISION 2013年第6期7卷 500-507页

作者： Sonmez, Elena Battini Albayrak, Songul Istanbul Bilgi Univ Dept Comp Engn Istanbul Turkey Yildiz Tech Univ Dept Comp Engn Istanbul Turkey

In recent years, the growing attention in the study of the compressive sensing (CS) theory suggested a novel classification algorithm called sparse representation-based classifier (SRC), which obtained promising results by casting classification as a sparse representation problem. Whereas SRC has been applied to different fields of applications and several variations of it have been proposed, less attention has been given to its critical parameters, that is, measurements correlated to its performance. This work underlines the differences between CS and SRC, it gives a mathematical definition of five measurements possible correlated to the performance of SRC and identifies three of them as critical parameters. The knowledge of the critical parameters is necessary to fuse multiple scores of SRC classifiers allowing for classification. The authors addressed the problem of two-dimensional face classification: using the Extended Yale B dataset to monitor the critical parameters and the Extended Cohn-Kanade database to test the robustness of SRC with emotional faces. Finally, the authors increased the initial performance of the holistic SRC with a block-based SRC, which uses one critical parameter for automatic selection of the most successful blocks.

关键词： face recognition image classification compressed sensing sparse representation-based classifier compressive sensing theory block-based SRC holistic SRC emotional face extended Cohn-Kanade database extended Yale B dataset two-dimensional face classification SRC classifiers CS

来源：评论

学校读者我要写书评

暂无评论

Retinal Vessel Centerline Extraction Using Multiscale Matched Filter and sparse representation-based classifier

Retinal Vessel Centerline Extraction Using Multiscale Matche...

引用

2nd International Conference on Medical Biometrics

作者： Zhang, Bob Li, Qin Zhang, Lei You, Jane Karray, Fakhri Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Hong Kong Polytech Univ Dept Comp Kowloon Peoples R China

ISBN: (纸本)9783642139222

Retina located in the back of the eye contains useful information in the diagnosis of certain diseases. By locating a blood vessel's width, color, reflectivity, tortuosity and abnormal branching, one can deduce the existence of these diseases. In order for this to be achieved, blood vessels first need to be extracted from its background in fundus image. In this paper we propose a new method to extract vessels based on Multiscale Production of Matched Filter (MPMF) and sparse representation-based classifier (SRC). First, we locate vessel centerline candidates using multi-scale Gaussian filtering, scale production, double thresholding and centerline detection. Then, the candidates which are centerline pixels are classified with SRC. Particularly, two dictionary elements of vessel and non-vessel are used in the SRC process. Experimental results on two public databases show that the proposed method is good at distinguishing vessel from non-vessel objects and extracting the centerlines of small vessels.

关键词： retinal vessel Matched Filter sparse representation-based classifier multiscale Gaussian filtering

来源：评论

学校读者我要写书评

暂无评论

Automatic Analysis of Tongue Substance Color and Coating Color Using sparse representation-based classifier 4

Automatic Analysis of Tongue Substance Color and Coating Col...

引用

4th IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Qu, Panling Zhang, Hui Zhuo, Li Zhang, Pei Zhang, Jing Beijing Univ Technol Signal & Informat Proc Lab Beijing Peoples R China

ISBN: (纸本)9781509034840

sparse representation-based classifier (SRC) is less sensitive to the shortage of data and the selection of feature space. In this paper, SRC is adopted to perform automatic analysis of tongue substance color and coating color which is considered as small dataset classification task. Firstly, for both training samples and testing samples, the tongue body regions are segmented, the regions of tongue substance and coating are separated and the features of these regions are extracted. Then, the feature vectors of testing samples are represented by sparse coding. Finally, the feature vectors of testing samples are reconstructed for each category, the residuals are calculated and the category of the smallest residual is determined as the classification result. Experimental results show that the proposed method can achieve the classification accuracy of 87.80% and 90.51% for the colors of tongue substance and tongue coating respectively. In addition, the classification accuracy of tongue coating color can reach to 91.19% with the assistance of visual samples by SRC. The proposed method provides a new method for automatic analysis of tongue color, and it is also a new application of SRC.

关键词： Traditional Chinese Medical tongue diagnosis substance color coating color sparse representation-based classifier small dataset visual samples

来源：评论

学校读者我要写书评

暂无评论

Automatic Analysis of Tongue Substance Color and Coating Color Using sparse representation-based classifier

Automatic Analysis of Tongue Substance Color and Coating Col...

引用

2016 IEEE International Conference on Progress in Informatics and Computing

作者： Panling Qu Hui Zhang Li Zhuo Pei Zhang Jing Zhang Signal and Information Processing Laboratory Beijing University of Technology

sparse representation-based classifier(SRC) is less sensitive to the shortage of data and the selection of feature space. In this paper, SRC is adopted to perform automatic analysis of tongue substance color and coating color which is considered as small dataset classification task. Firstly, for both training samples and testing samples, the tongue body regions are segmented, the regions of tongue substance and coating are separated and the features of these regions are extracted. Then, the feature vectors of testing samples are represented by sparse coding. Finally, the feature vectors of testing samples are reconstructed for each category, the residuals are calculated and the category of the smallest residual is determined as the classification result. Experimental results show that the proposed method can achieve the classification accuracy of 87.80% and 90.51% for the colors of tongue substance and tongue coating respectively. In addition, the classification accuracy of tongue coating color can reach to 91.19% with the assistance of visual samples by SRC. The proposed method provides a new method for automatic analysis of tongue color, and it is also a new application of SRC.

关键词： Traditional Chinese Medical tongue diagnosis substance color coating color sparse representation-based classifier small dataset visual samples

来源：评论

学校读者我要写书评

暂无评论

Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2022年第Pt A期151卷 106268-106268页

作者： Guo, Xiaoyi Tiwari, Prayag Zhang, Ying Han, Shuguang Wang, Yansu Ding, Yijie Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Quzhou 324000 Peoples R China Halmstad Univ Sch Informat Technol Halmstad Sweden Beidahuang Ind Grp Gen Hosp Harbin 150088 Peoples R China

DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase, prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of the conventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years, a variety of machine learning-based methods that have been used on a large scale for large-scale screening of DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparse representation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solve nonlinear classification problems. And L2,1-matrix norm is introduced to get sparse solution of model. To evaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRC achieves better performance in experimental results.

关键词： Sequence classification Biological sequence features Random features sparse representation-based classifier

来源：评论

学校读者我要写书评

暂无评论

sparse representation classifier Guided Grassmann Reconstruction Metric Learning With Applications to Image Set Analysis

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 4307-4322页

作者： Wei, Dong Shen, Xiaobo Sun, Quansen Gao, Xizhan Ren, Zhenwen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China Southwest Univ Sci & Technol Sch Natl Def Sci & Technol Mianyang 621010 Peoples R China

Modeling a sequence of video frames as a linear subspace on Grassmann manifold has recently become increasingly attractive in multiple computer vision applications. The success of such algorithms largely depends on a good distance measure, and learning an appropriate metric on Grassmann manifold remains a key challenge. Existing works address this by learning a discriminative mapping from the original Grassmann manifold to Hilbert space or a lower-dimensional, more discriminative Grassmann manifold. However, these approaches always highly rely on nearest neighbor matching of samples on the projected space, which is sensitive to noises and errors. Different from them, this paper proposes a Grassmann Reconstruction Metric Learning (GRML) algorithm guided by sparse representation-based classifier (SRC) for image set classification. SRC selects the coefficients associated with each class to reconstruct training samples, and then we employ it as a criterion to direct the design of a discriminant metric on Grassmann manifold. Specifically, GRML attempts to jointly maximize the inter-class reconstruction residual and minimize the intra-class reconstruction residual in the lower but more discriminative Grassmann manifold. To further explore the intrinsic geometry distance, we present a Grassmann Reconstruction Multiple Kernel Metric Learning (GRMKML) algorithm, which aims to jointly learn a metric and the corresponding kernel from a family of kernels for Grassmann manifold. Extensive experiments on eight benchmark datasets demonstrate that the proposed algorithms perform favorably against the state-of-the-art methods.

关键词： Grassmann manifold image set classification multiple kernel learning sparse representation-based classifier

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：