| Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection | |
Cong Y(丛杨) ; Yuan JS(袁浚菘); Luo JB(罗杰波)
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| Department | 机器人学研究室 |
| Source Publication | IEEE TRANSACTIONS ON MULTIMEDIA
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| ISSN | 1520-9210 |
| 2012 | |
| Volume | 14Issue:1Pages:66-75 |
| Indexed By | SCI ; EI |
| EI Accession number | 20120514724056 |
| WOS ID | WOS:000302701100007 |
| Contribution Rank | 1 |
| Funding Organization | 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. |
| Keyword | Group Sparse Key Frame Lasso Scene Analysis Video Analysis Video Skim Video Summarization |
| Abstract | 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. |
| Language | 英语 |
| WOS Headings | Science & Technology ; Technology |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| WOS Keyword | REPRESENTATION ; EXTRACTION ; FRAMEWORK ; MODEL |
| WOS Research Area | Computer Science ; Telecommunications |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sia.cn/handle/173321/10040 |
| Collection | 机器人学研究室 |
| Corresponding Author | Cong Y(丛杨) |
| Affiliation | 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 |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Towards Scalable Sum（2123KB） | 期刊论文 | 作者接受稿 | 开放获取 | ODC PDDL | View Application Full Text | |
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