UM
Learning double weights via data augmentation for robust sparse and collaborative representation-based classification
Zeng,Shaoning1,2; Zhang,Bob2; Gou,Jianping3
2020-08-01
Source PublicationMultimedia Tools and Applications
ISSN1380-7501
Volume79Issue:29-30Pages:20617-20638
AbstractImage classification is a hot technique applied in many multimedia systems, where both l and l regularizations have shown potential for robust sparse representation-based image classification. However, previous studies showed that l or l alone cannot ensure a robust result. The robustness of a classifier depends on the nature of the dataset most of the time. What is worse, data augmentation may make the dataset more complicated, which leads a sparse model become harder to optimize. In this paper, a novel sparse representation that learns double weights through data augmentation is proposed for robust image classification. The first weight combines the two coefficients solved by l and l regularizations to obtain a more discriminative representation, while the second weight integrates the residuals obtained from the original and virtual samples, to take full advantage of diversity created by data augmentation. The double-weight process builds a robust model that is able to deal with the augmented but variational datasets. Experiments on popular facial and object datasets demonstrate the promising performance of the proposed method. Learning double weights via sample virtualization is helpful to develop multimedia applications.
KeywordAugmentation Image classification Regularization Sparse representation
DOI10.1007/s11042-020-08918-2
URLView the original
Language英语
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang,Bob
Affiliation1.School of Computer Science and Engineering,Huizhou University,Huizhou,China
2.Pattern Analysis and Machine Intelligence Group,Department of Computer and Information Science,University of Macau,Macau,China
3.College of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zeng,Shaoning,Zhang,Bob,Gou,Jianping. Learning double weights via data augmentation for robust sparse and collaborative representation-based classification[J]. Multimedia Tools and Applications,2020,79(29-30):20617-20638.
APA Zeng,Shaoning,Zhang,Bob,&Gou,Jianping.(2020).Learning double weights via data augmentation for robust sparse and collaborative representation-based classification.Multimedia Tools and Applications,79(29-30),20617-20638.
MLA Zeng,Shaoning,et al."Learning double weights via data augmentation for robust sparse and collaborative representation-based classification".Multimedia Tools and Applications 79.29-30(2020):20617-20638.
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