Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection

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	Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection
	Cong Y(丛杨); Yuan JS(袁浚菘); Luo JB(罗杰波)
作者部门	机器人学研究室
关键词	Group Sparse Key Frame Lasso Scene Analysis Video Analysis Video Skim Video Summarization
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2012
卷号	14 期号:1 页码:66-75
收录类别	SCI ; EI
EI收录号	20120514724056
WOS记录号	WOS:000302701100007
产权排序	1
资助机构	This work was done when C. Yang was a research fellow at Nanyang Technological University and was supported in part by the Nanyang Assistant Professorship (SUG M58040015) to Dr. J. Yuan and NSFC (61105013). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Changsheng Xu.
摘要	The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is more challenging because of its unconstrained content and the lack of any pre-imposed video structures. We formulate video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary. An efficient global optimization algorithm is introduced to solve the dictionary selection model with the convergence rates as O(1/root K-2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1/root K). Our method provides a scalable solution for both key frame extraction and video skim generation, because one can select an arbitrary number of key frames to represent the original videos. Experiments on a human labeled benchmark dataset and comparisons to the state-of-the-art methods demonstrate the advantages of our algorithm.
语种	英语
WOS标题词	Science & Technology ; Technology
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
关键词[WOS]	REPRESENTATION ; EXTRACTION ; FRAMEWORK ; MODEL
WOS研究方向	Computer Science ; Telecommunications
引用统计
文献类型	期刊论文
条目标识符	http://ir.sia.cn/handle/173321/10040
专题	机器人学研究室
通讯作者	Cong Y(丛杨)
作者单位	1.Department of EEE, Nanyang Technological University, Singapore 639798, Singapore 2.Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China 3.Department of Computer Science, University of Rochester, Rochester, NY 14627, United States
推荐引用方式 GB/T 7714	Cong Y,Yuan JS,Luo JB. Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2012,14(1):66-75.
APA	Cong Y,Yuan JS,&Luo JB.(2012).Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection.IEEE TRANSACTIONS ON MULTIMEDIA,14(1),66-75.
MLA	Cong Y,et al."Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection".IEEE TRANSACTIONS ON MULTIMEDIA 14.1(2012):66-75.

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