HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization
文献类型:会议论文
作者 | Zhao, Bin1; Li, Xuelong2; Lu, Xiaoqiang2 |
出版日期 | 2018-12-14 |
会议日期 | 2018-06-18 |
会议地点 | Salt Lake City, UT, United states |
DOI | 10.1109/CVPR.2018.00773 |
页码 | 7405-7414 |
英文摘要 | 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. |
产权排序 | 2 |
会议录 | Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
会议录出版者 | IEEE Computer Society |
语种 | 英语 |
ISSN号 | 10636919 |
ISBN号 | 9781538664209 |
源URL | [http://ir.opt.ac.cn/handle/181661/31347] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
作者单位 | 1.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 |
推荐引用方式 GB/T 7714 | Zhao, Bin,Li, Xuelong,Lu, Xiaoqiang. HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization[C]. 见:. Salt Lake City, UT, United states. 2018-06-18. |
入库方式: OAI收割
来源:西安光学精密机械研究所
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