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Object-Based Access to TV Rushes Video

  • Alan F. Smeaton
  • Gareth J. F. Jones
  • Hyowon Lee
  • Noel E. O’Connor
  • Sorin Sav
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)

Abstract

Recent years have seen the development of different modalities for video retrieval. The most common of these are (1) to use text from speech recognition or closed captions, (2) to match keyframes using image retrieval techniques like colour and texture [6] and (3) to use semantic features like “indoor”, “outdoor” or “persons”. Of these, text-based retrieval is the most mature and useful, while image-based retrieval using low-level image features usually depends on matching keyframes rather than whole-shots. Automatic detection of video concepts is receiving much attention and as progress is made in this area we will see consequent impact on the quality of video retrieval. In practice it is the combination of these techniques which realises the most useful, and effective, video retrieval as shown by us repeatedly in TRECVid [5].

Keywords

Query Image Relevance Feedback Video Object Video Retrieval Segmented Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alan F. Smeaton
    • 1
  • Gareth J. F. Jones
    • 1
  • Hyowon Lee
    • 1
  • Noel E. O’Connor
    • 1
  • Sorin Sav
    • 1
  1. 1.Centre for Digital Video Processing & Adaptive Information ClusterDublin City UniversityGlasnevin, Dublin 9Ireland

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