Multimedia Tools and Applications

, Volume 76, Issue 6, pp 7947–7971 | Cite as

Scalable video summarization via sparse dictionary learning and selection simultaneously

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Abstract

Every day, a huge amount of video data is generated worldwide and processing this kind of data requires powerful resources in terms of time, manpower, and hardware. Therefore, to help quickly understand the content of video data, video summarization methods have been proposed. Recently, sparse formulation-based methods have been found to be able to summarize a large amount of video compared to other methods. In this paper, we propose a new method in which video summarization is performed as training and selection sparse dictionary problem simultaneously. It is shown that the proposed method is able to improve the summarization of a large amount of video data compared to other methods. Finally, the performance of the proposed method is compared to state-of-the-art methods using standard data sets, in which the key frames are manually tagged. The obtained results demonstrate that the proposed method improves video summarization compared to other methods.

Keywords

Video summarization Dictionary learning Key frames Scene analysis Video analysis Sparse coding 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Faculty of Electrical and Computer EngineeringUniversity of BirjandBirjandIran

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