Video Summarization via Simultaneous Block Sparse Representation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

4 Scopus Citations
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Author(s)

  • Mingyang Ma
  • Shaohui Mei
  • Shuai Wan
  • Zhiyong Wang
  • Dagan Feng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2017 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications (DICTA)
PublisherIEEE
Pages676-682
ISBN (Electronic)978-1-5386-2839-3
Publication statusPublished - Nov 2017

Conference

Title2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017
PlaceAustralia
CitySydney
Period29 November - 1 December 2017

Abstract

With the ever increasing volume of video content, efficient and effective video summarization (VS) techniques are urgently demanded to manage the large amount of video data. Recent developments on sparse representation based approaches have demonstrated promising results for VS. While most existing approaches treat each frame independently, in this paper, the block-sparsity, which means the keyframes or non-keyframes occur in blocks due to the content similarity in a same frame block, is taken into account. Therefore, the video summarization problem is formulated as a simultaneous block sparse representation model. For model optimization, simultaneous block orthogonal matching pursuit (SBOMP) algorithms are designed to extract keyframes. Experimental results on a benchmark dataset with various types of videos demonstrate that the proposed algorithms can not only outperform the state of the art, but also reduce the probability of selecting non-informative frames and "outlier" frames.

Research Area(s)

  • Block-sparsity, Frame block, Matching pursuit, Video summarization

Citation Format(s)

Video Summarization via Simultaneous Block Sparse Representation. / Ma, Mingyang; Mei, Shaohui; Wan, Shuai; Hou, Junhui; Wang, Zhiyong; Feng, Dagan.

2017 International Conference on Digital Image Computing : Techniques and Applications (DICTA). IEEE, 2017. p. 676-682.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review