Sparse Representation and Collaborative Representation? Both Help Image Classification

被引:3
作者
Xie, Wen-Yang [1 ]
Liu, Bao-Di [1 ]
Shao, Shuai [1 ]
Li, Ye [2 ]
Wang, Yan-Jiang [1 ]
机构
[1] China Univ Petr Huadong, Coll Informat & Control Engn, Qingdao 266580, Shandong, Peoples R China
[2] Qilu Univ Technol, Shandong Comp Sci Ctr, Shandong Prov Key Lab Comp Networks, Jinan 250353, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Collaborative representation based classification; elastic-net regularized; image classification; kernel space; sparse representation based classification; FACE RECOGNITION; KERNEL;
D O I
10.1109/ACCESS.2019.2921538
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Image classification has attracted more and more attention. During the past decades, image classification has shown growing interest in representation-based classification methods, such as sparse representation-based classification and collaborative representation-based classification. However, the available representation-based methods still suffer from some problems. Especially, most methods only consider the shared representation of a test image. In this paper, we propose an elastic-net regularized regression algorithm (ENRR) for image classification. Specifically, our proposed method combines shared sparse representation with class specific collaborative representation when representing the test sample. Moreover, we extend the proposed ENRR to arbitrary kernel space to achieve better classification performance due to specificities and complexities of original images. The extensive experiments on face recognition datasets, handwritten recognition datasets, and remote sensing image datasets clearly demonstrate that the proposed ENRR outperforms several conventional methods in classification accuracy.
引用
收藏
页码:76061 / 76070
页数:10
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