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Unsupervised learning of human action categories in still images with deep representations
Zheng, Yunpeng1; Li, Xuelong2; Lu, Xiaoqiang3
作者部门光谱成像技术研究室
2019-12
发表期刊ACM Transactions on Multimedia Computing, Communications and Applications
ISSN15516857;15516865
卷号15期号:4
产权排序3
摘要

In this article, we propose a novel method for unsupervised learning of human action categories in still images. In contrast to previous methods, the proposed method explores distinctive information of actions directly from unlabeled image databases, attempting to learn discriminative deep representations in an unsupervised manner to distinguish different actions. In the proposed method, action image collections can be used without manual annotations. Specifically, (i) to deal with the problem that unsupervised discriminative deep representations are difficult to learn, the proposed method builds a training dataset with surrogate labels from the unlabeled dataset, then learns discriminative representations by alternately updating convolutional neural network (CNN) parameters and the surrogate training dataset in an iterative manner; (ii) to explore the discriminatory information among different action categories, training batches for updating the CNN parameters are built with triplet groups and the triplet loss function is introduced to update the CNN parameters; and (iii) to learn more discriminative deep representations, a Random Forest classifier is adopted to update the surrogate training dataset, and more beneficial triplet groups then can be built with the updated surrogate training dataset. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the proposed method. © 2019 Association for Computing Machinery.

关键词Action categorization unsupervised learning deep representations
DOI10.1145/3362161
收录类别SCI ; EI
语种英语
WOS记录号WOS:000512285800012
出版者Association for Computing Machinery
EI入藏号20195207939683
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/83119
专题光谱成像技术研究室
通讯作者Lu, Xiaoqiang
作者单位1.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and PrecisionMechanics, ChineseAcademy of Sciences, University of ChineseAcademy of Sciences, China;
2.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China;
3.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, China
推荐引用方式
GB/T 7714
Zheng, Yunpeng,Li, Xuelong,Lu, Xiaoqiang. Unsupervised learning of human action categories in still images with deep representations[J]. ACM Transactions on Multimedia Computing, Communications and Applications,2019,15(4).
APA Zheng, Yunpeng,Li, Xuelong,&Lu, Xiaoqiang.(2019).Unsupervised learning of human action categories in still images with deep representations.ACM Transactions on Multimedia Computing, Communications and Applications,15(4).
MLA Zheng, Yunpeng,et al."Unsupervised learning of human action categories in still images with deep representations".ACM Transactions on Multimedia Computing, Communications and Applications 15.4(2019).
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