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HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization
Zhao, Bin1; Li, Xuelong2; Lu, Xiaoqiang2
2018-12-14
Conference Name31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Source PublicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Pages7405-7414
Conference Date2018-06-18
Conference PlaceSalt Lake City, UT, United states
PublisherIEEE Computer Society
Contribution Rank2
Abstract

Although video summarization has achieved great success in recent years, few approaches have realized the influence of video structure on the summarization results. As we know, the video data follow a hierarchical structure, i.e., a video is composed of shots, and a shot is composed of several frames. Generally, shots provide the activity-level information for people to understand the video content. While few existing summarization approaches pay attention to the shot segmentation procedure. They generate shots by some trivial strategies, such as fixed length segmentation, which may destroy the underlying hierarchical structure of video data and further reduce the quality of generated summaries. To address this problem, we propose a structure-adaptive video summarization approach that integrates shot segmentation and video summarization into a Hierarchical Structure-Adaptive RNN, denoted as HSA-RNN. We evaluate the proposed approach on four popular datasets, i.e., SumMe, TVsum, CoSum and VTW. The experimental results have demonstrated the effectiveness of HSA-RNN in the video summarization task. © 2018 IEEE.

Department光学影像学习与分析中心
DOI10.1109/CVPR.2018.00773
Indexed ByEI
ISBN9781538664209
Language英语
ISSN10636919
EI Accession Number20191106643008
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31347
Collection光学影像学习与分析中心
Affiliation1.School of Computer Science, Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, China;
2.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi, China
Recommended Citation
GB/T 7714
Zhao, Bin,Li, Xuelong,Lu, Xiaoqiang. HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization[C]:IEEE Computer Society,2018:7405-7414.
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