Advertisement

Multimedia Tools and Applications

, Volume 26, Issue 2, pp 153–173 | Cite as

The CPR Model for Summarizing Video

  • Marat Fayzullin
  • V. S. Subrahmanian
  • Antonio Picariello
  • Maria Luisa Sapino
Article

Abstract

Most past work on video summarization has been based on selecting key frames from videos. We propose a model of video summarization based on three important parameters: Priority (of frames), Continuity (of the summary), and non-Repetition (of the summary). In short, a summary must include high priority frames and must be continuous and non-repetitive. An optimal summary is one that maximizes an objective function based on these three parameters. We show examples of how CPR parameters can be computed and provide algorithms to find optimal summaries based on the CPR approach. Finally, we briefly report on the performance of these algorithms.

Keywords

multimedia video databases summarization framework algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. Adali, K.S. Candan, S.-S. Chen, K. Erol, and V.S. Subrahmanian, “Advanced video information systems,” ACM Multimedia Systems Journal, Vol. 4, pp. 172–186, 1996.CrossRefGoogle Scholar
  2. 2.
    T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, 2nd Edition MIT Press, 2001.Google Scholar
  3. 3.
    D. DeMenthon, D.S. Doermann, and V. Kobla, “Video summarization by curve simplification,” in Proc. ACM Multimedia, Bristol, England, 1998, pp. 211–218.Google Scholar
  4. 4.
    L. He, E. Sanocki, A. Gupta, and J. Grudin, “Auto-summarization of audio-video presentations,” in ACM Proc. on Multimedia, 1999, pp. 489–498.Google Scholar
  5. 5.
    S. Ju, M. Black, S. Minneman, and D. Kimber, “Summarization of videotaped presentations: Automatic analysis of motion and gesture,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 8, No. 5, pp. 686–696, 1998.CrossRefGoogle Scholar
  6. 6.
    Y.P. Ma, L. Lu, H.J. Zhang, and M. Li, “A user attention model for video summarization,” in Proc. ACM Multimedia, 2002.Google Scholar
  7. 7.
    H. Martin and R. Lozano, “Dynamic generation of video abstracts using an object oriented video DBMS,” Networking and Information Systems Journal, Vol. 3, No. 1, pp. 53–75, 2000.Google Scholar
  8. 8.
    H.R. Naphide and T.S. Huang, “A probabilistic framework for semantic video indexing, filtering, and retrieval,” IEEE Transactions on Multimedia, Vol. 3, No. 1, pp. 141–151, 2001.CrossRefGoogle Scholar
  9. 9.
    E. Oomoto and K. Tanaka, “OVID: Design and implementation of a video-object database system,” IEEE TKDE (Multimedia Information Systems), Vol. 5, No. 4, pp. 629–643, 1993.Google Scholar
  10. 10.
    C. Stauffer and E. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. on pattern analysis and machine intelligence, Vol. 22, No. 8, pp. 747–757, 2000.CrossRefGoogle Scholar
  11. 11.
    V.S. Subrahmanian, Principles of Multimedia Database Systems, Morgan Kaufmann, 1998.Google Scholar
  12. 12.
    D. Zhong and S.F. Chang, “Video object model and segmentation for content-based video indexing,” in IEEE Intern. Conf. on Circuits and Systems, Hong Kong, June, 1997.Google Scholar
  13. 13.
    W. Zhou, A. Vellaikal, and C.C. Jay Kuo, “Rule-based video classification system for basketball video indexing,” in Proc. ACM Multimedia Workshop, 2000, pp. 213–216.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Marat Fayzullin
    • 1
  • V. S. Subrahmanian
    • 1
  • Antonio Picariello
    • 2
  • Maria Luisa Sapino
    • 3
  1. 1.Department of Computer ScienceUniversity of MarylandCollege Park
  2. 2.Dipartimento di Informatica e SistemisticaUniversità di Napoli “Federico IINapoliItaly
  3. 3.Dipartimento di InformaticaMaria Luisa Sapino, Università di TorinoTorinoItaly

Personalised recommendations