A General Framework for Edited Video and Raw Video Summarization | |
Li, Xuelong1; Zhao, Bin2; Lu, Xiaoqiang1 | |
作者部门 | 光学影像学习与分析中心 |
2017-08-01 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
卷号 | 26期号:8页码:3652-3664 |
产权排序 | 1 |
摘要 | In this paper, we build a general summarization framework for both of edited video and raw video summarization. Overall, our work can be divided into three folds. 1) Four models are designed to capture the properties of video summaries, i.e., containing important people and objects (importance), representative to the video content (representativeness), no similar key-shots (diversity), and smoothness of the storyline (storyness). Specifically, these models are applicable to both edited videos and raw videos. 2) A comprehensive score function is built with the weighted combination of the aforementioned four models. Note that the weights of the four models in the score function, denoted as property-weight, are learned in a supervised manner. Besides, the property-weights are learned for edited videos and raw videos, respectively. 3) The training set is constructed with both edited videos and raw videos in order to make up the lack of training data. Particularly, each training video is equipped with a pair of mixing-coefficients, which can reduce the structure mess in the training set caused by the rough mixture. We test our framework on three data sets, including edited videos, short raw videos, and long raw videos. Experimental results have verified the effectiveness of the proposed framework. |
文章类型 | Article |
关键词 | Video Summary Score Function Property-weight Mixing-coefficient |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIP.2017.2695887 |
收录类别 | SCI ; EI |
关键词[WOS] | EGOCENTRIC VIDEO ; NETWORKS |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | National Natural Science Foundation of China(61472413 ; Chinese Academy of Sciences(KGZD-EWT03 ; Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408) ; 61761130079) ; QYZDB-SSW-JSC015) |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000403819200001 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29034 |
专题 | 光谱成像技术研究室 |
通讯作者 | Lu, Xiaoqiang |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning, Xian 710119, Peoples R China 2.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning, Xian 710072, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Xuelong,Zhao, Bin,Lu, Xiaoqiang. A General Framework for Edited Video and Raw Video Summarization[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(8):3652-3664. |
APA | Li, Xuelong,Zhao, Bin,&Lu, Xiaoqiang.(2017).A General Framework for Edited Video and Raw Video Summarization.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(8),3652-3664. |
MLA | Li, Xuelong,et al."A General Framework for Edited Video and Raw Video Summarization".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.8(2017):3652-3664. |
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 | ||
A General Framework (4575KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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