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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)
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
ISSN1520-9210
2012
Volume14Issue:1Pages:66-75
Indexed BySCI ; EI
EI Accession number20120514724056
WOS IDWOS:000302701100007
Contribution Rank1
Funding OrganizationThis 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.
KeywordGroup Sparse Key Frame Lasso Scene Analysis Video Analysis Video Skim Video Summarization
AbstractThe 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 HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS KeywordREPRESENTATION ; EXTRACTION ; FRAMEWORK ; MODEL
WOS Research AreaComputer Science ; Telecommunications
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/10040
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.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.
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