Video summarization via temporal collaborative representation of adjacent frames

Mingyang Ma, Shaohui Met, Junhui Hou, Shuai Wan, Zhiyong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

The ever increasing volume of video content demands to develop efficient and effective video summarization (VS) techniques to manage the video data. Recent developments on sparse representation have demonstrated prospective results for VS. In this paper, in consideration of visual similarity of adjacent frames, we formulate the video summarization problem with a temporal collaborative representation (TCR) model, in which the adjacent frames instead of an individual frame are taken into consideration to avoid selecting transitional frames. In addition, a greedy iterative algorithm is designed for model optimization. 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 transitional frames.

Original languageEnglish
Title of host publication2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages164-169
Number of pages6
ISBN (Electronic)9781538621592
DOIs
StatePublished - 2 Jul 2017
Event25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Xiamen, China
Duration: 6 Nov 20179 Nov 2017

Publication series

Name2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
Volume2018-January

Conference

Conference25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017
Country/TerritoryChina
CityXiamen
Period6/11/179/11/17

Keywords

  • Keyframe
  • Temporal collaborative representation
  • Video summarization

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