Sparse Representation and Collaborative Representation? Both Help Image Classification
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...
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doaj-c35cd93f1dc047e5a852c6449b3b8ff52021-03-29T23:02:13ZengIEEEIEEE Access2169-35362019-01-017760617607010.1109/ACCESS.2019.29215388733018Sparse Representation and Collaborative Representation? Both Help Image ClassificationWen-Yang Xie0https://orcid.org/0000-0002-1863-3573Bao-Di Liu1Shuai Shao2Ye Li3Yan-Jiang Wang4College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao, ChinaCollege of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao, ChinaCollege of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao, ChinaShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center, Qilu University of Technology, Jinan, ChinaCollege of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao, ChinaImage 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.https://ieeexplore.ieee.org/document/8733018/Collaborative representation based classificationelastic-net regularizedimage classificationkernel spacesparse representation based classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wen-Yang Xie Bao-Di Liu Shuai Shao Ye Li Yan-Jiang Wang |
spellingShingle |
Wen-Yang Xie Bao-Di Liu Shuai Shao Ye Li Yan-Jiang Wang Sparse Representation and Collaborative Representation? Both Help Image Classification IEEE Access Collaborative representation based classification elastic-net regularized image classification kernel space sparse representation based classification |
author_facet |
Wen-Yang Xie Bao-Di Liu Shuai Shao Ye Li Yan-Jiang Wang |
author_sort |
Wen-Yang Xie |
title |
Sparse Representation and Collaborative Representation? Both Help Image Classification |
title_short |
Sparse Representation and Collaborative Representation? Both Help Image Classification |
title_full |
Sparse Representation and Collaborative Representation? Both Help Image Classification |
title_fullStr |
Sparse Representation and Collaborative Representation? Both Help Image Classification |
title_full_unstemmed |
Sparse Representation and Collaborative Representation? Both Help Image Classification |
title_sort |
sparse representation and collaborative representation? both help image classification |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
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. |
topic |
Collaborative representation based classification elastic-net regularized image classification kernel space sparse representation based classification |
url |
https://ieeexplore.ieee.org/document/8733018/ |
work_keys_str_mv |
AT wenyangxie sparserepresentationandcollaborativerepresentationbothhelpimageclassification AT baodiliu sparserepresentationandcollaborativerepresentationbothhelpimageclassification AT shuaishao sparserepresentationandcollaborativerepresentationbothhelpimageclassification AT yeli sparserepresentationandcollaborativerepresentationbothhelpimageclassification AT yanjiangwang sparserepresentationandcollaborativerepresentationbothhelpimageclassification |
_version_ |
1724190254295941120 |