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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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.76061-76070
Main Authors: Xie, Wen-Yang, Liu, Bao-Di, Shao, Shuai, Li, Ye, Wang, Yan-Jiang
Format: Article
Language:English
Subjects:
Algorithms
Classification
Collaboration
Collaborative representation based classification
Datasets
Dictionaries
elastic-net regularized
Face recognition
Handwriting recognition
Image classification
Image recognition
Kernel
kernel space
Linear programming
Object recognition
Remote sensing
Representations
sparse representation based classification
Training
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2921538