• DocumentCode
    778044
  • Title

    Video annotation based on temporally consistent Gaussian random field

  • Author

    Tang, J. ; Hua, X.-S. ; Mei, T. ; Qi, G.-J. ; Wu, X.

  • Author_Institution
    Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei
  • Volume
    43
  • Issue
    8
  • fYear
    2007
  • Firstpage
    448
  • Lastpage
    449
  • Abstract
    A novel method for automatically annotating video semantics, called temporally consistent Gaussian random field (TCGRF) is proposed. Since the temporally adjacent video segments (e.g. shots) usually have a similar semantic concept, TCGRF adapts the temporal consistency property of video data into graph-based semi-supervised learning to improve the annotation results. Experiments conducted on the TRECVID data set have demonstrated its effectiveness
  • Keywords
    content-based retrieval; feature extraction; video signal processing; graph-based semi-supervised learning; temporally consistent Gaussian random field; video annotation; video data; video semantics;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:20073674
  • Filename
    4155592
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=778044