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A novel clustering method for static video summarization

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Abstract

Static video summarization is recognized as an effective way for users to quickly browse and comprehend large numbers of videos. In this paper, we formulate static video summarization as a clustering problem. Inspired by the idea from high density peaks search clustering algorithm, we propose an effective clustering algorithm by integrating important properties of video to gather similar frames into clusters. Finally, all clusters’ center will be collected as static video summarization. Compared with existing clustering-based video summarization approaches, our work can detect frames which are highly relevant and generate representative clusters automatically. We evaluate our proposed work by comparing it with several state-of-the-art clustering-based video summarization methods and some classical clustering algorithms. The experimental results evidence that our proposed method has better performance and efficiency.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61502311), Natural Science Foundation of Guangdong Province (No. 2016A030310053), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), the Science and Technology Innovation Commission of Shenzhen under Grant (No. JCYJ20150324141711640), and the Shenzhen University research funding (201535).

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Authors and Affiliations

  1. College of Computer Science & Software Engineering, Shenzhen University, Shenzhen, Guang Dong, People’s Republic of China

    Jiaxin Wu, Sheng-hua Zhong & Jianmin Jiang

  2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guang Dong, People’s Republic of China

    Yunyun Yang

Authors
  1. Jiaxin Wu
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  2. Sheng-hua Zhong
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  3. Jianmin Jiang
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  4. Yunyun Yang
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Correspondence to Sheng-hua Zhong.

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Wu, J., Zhong, Sh., Jiang, J. et al. A novel clustering method for static video summarization. Multimed Tools Appl 76, 9625–9641 (2017). https://doi.org/10.1007/s11042-016-3569-x

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