HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization | |
Zhao, Bin1; Li, Xuelong2; Lu, Xiaoqiang2 | |
2018-12-14 | |
会议名称 | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
会议录名称 | Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
页码 | 7405-7414 |
会议日期 | 2018-06-18 |
会议地点 | Salt Lake City, UT, United states |
出版者 | IEEE Computer Society |
产权排序 | 2 |
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. |
作者部门 | 光谱成像技术研究室 |
DOI | 10.1109/CVPR.2018.00773 |
收录类别 | EI |
ISBN号 | 9781538664209 |
语种 | 英语 |
ISSN号 | 10636919 |
EI入藏号 | 20191106643008 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/31347 |
专题 | 光谱成像技术研究室 |
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]:IEEE Computer Society,2018:7405-7414. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
HSA-RNN Hierarchica(803KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论