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Long-Short-Term Features for Dynamic Scene Classification
Huang, Yuanjun1,2; Cao, Xianbin1,2; Wang, Qi3,4; Zhang, Baochang5; Zhen, Xiantong1,2; Li, Xuelong6,7
作者部门光谱成像技术研究室
2019-04
发表期刊IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215;1558-2205
卷号29期号:4页码:1038-1047
产权排序6
摘要

Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long-short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods.

关键词Dynamic scene classification long-short term feature long term frequency feature
DOI10.1109/TCSVT.2018.2823360
收录类别SCI
语种英语
WOS记录号WOS:000464149700010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
被引频次:39[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/181661/31374
专题光谱成像技术研究室
通讯作者Cao, Xianbin
作者单位1.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
2.Minist Ind & Informat Technol China, Key Lab Adv Technol Near Space Informat Syst, Beijing 100031, Peoples R China
3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
4.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning, Xian 710072, Shaanxi, Peoples R China
5.Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
6.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Huang, Yuanjun,Cao, Xianbin,Wang, Qi,et al. Long-Short-Term Features for Dynamic Scene Classification[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2019,29(4):1038-1047.
APA Huang, Yuanjun,Cao, Xianbin,Wang, Qi,Zhang, Baochang,Zhen, Xiantong,&Li, Xuelong.(2019).Long-Short-Term Features for Dynamic Scene Classification.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,29(4),1038-1047.
MLA Huang, Yuanjun,et al."Long-Short-Term Features for Dynamic Scene Classification".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 29.4(2019):1038-1047.
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