Low complexity on-line video summarization with Gaussian mixture model based clustering

Shun Hsing Ou, Chia-Han Lee, V. Srinivasa Somayazulu, Yen Kuang Chen, Shao Yi Chien

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

7 Scopus citations

Abstract

Techniques of video summarization have attracted significant research interests in the past decade due to the rapid progress in video recording, computation, and communication technologies. However, most of the existing methods analyze the video in an off-line manner, which greatly reduces the flexibility of the system. On-line summarization, which can progressively process video during video recording, is then proposed for a wide range of applications. In this paper, an on-line summarization method using Gaussian mixture model is proposed. As shown in the experiments, the proposed method outperforms other on-line methods in both summarization quality and computational efficiency. It can generate summarization with a shorter latency and much lower computation resource requirements.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1260-1264
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - 1 Jan 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 4 May 20149 May 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period4/05/149/05/14

Keywords

  • Gaussian mixture model
  • On-line video summarization
  • Video skimming
  • Video Summarization

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