Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97228
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dc.contributor.authorCong, Yangen
dc.contributor.authorYuan, Junsongen
dc.contributor.authorLuo, Jieboen
dc.date.accessioned2013-07-15T08:37:38Zen
dc.date.accessioned2019-12-06T19:40:23Z-
dc.date.available2013-07-15T08:37:38Zen
dc.date.available2019-12-06T19:40:23Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.citationCong, Y., Yuan, J., & Luo, J. (2012). Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection. IEEE Transactions on Multimedia, 14(1), 66-75.en
dc.identifier.issn1520-9210en
dc.identifier.urihttps://hdl.handle.net/10356/97228-
dc.identifier.urihttp://hdl.handle.net/10220/11469en
dc.description.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/K2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1/√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.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on multimediaen
dc.rights© 2011 IEEE.en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleTowards scalable summarization of consumer videos via sparse dictionary selectionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doihttp://dx.doi.org/10.1109/TMM.2011.2166951en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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