skip to main content
10.1145/951676.951679acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
Article

The CPR model for summarizing video

Published:07 November 2003Publication History

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, must be continuous and non-repetitive. An optimal summary is one that maximizes an objective function based on these three parameters. We develop formal definitions of all these concepts and provide algorithms to find optimal summaries. We briefly report on the performance of these algorithms.

References

  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, 1996, pp. 172--186. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein. "Introduction to Algorithms, 2nd Edition" MIT Press, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. DeMenthon, D.S. Doermann, and V. Kobla. Video Summarization by Curve Simplification. Proc. ACM Multimedia, Bristol, England, 1998, pp. 211--218. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. L. He, E. Sanocki, A. Gupta, and J. Grudin. Auto-Summarization of Audio-Video Presentations. ACM Proc. on Multimedia, 1999, pp. 489--498. Google ScholarGoogle ScholarDigital LibraryDigital Library
  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(5), 1998, pp. 686--696. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Y.P. Ma, L. Lu, H.J. Zhang, and M. Li. A User Attention Model for Video Summarization. Proc. ACM Multimedia, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H.Martin and R.Lozano. Dynamic Generation of Video Abstracts Using an Object Oriented Video DBMS. Networking and Information Systems Journal, Vol. 3(1), 2000, pp. 53--75.Google ScholarGoogle Scholar
  8. H.R. Naphide and T.S. Huang. A Probabilistic Framework for Semantic Video Indexing, Filtering, and Retrieval. IEEE Transactions on Multimedia, Vol. 3(1), 2001, pp. 141--151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. E. Oomoto and K. Tanaka. OVID: Design and Implementation of a Video-Object Database System. IEEE TKDE (Multimedia Information Systems, Vol. 5(4), 1993, pp. 629--643. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. C. Stauffer and E. Frimson. Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22(8), 2000, pp 747--757. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. V.S. Subrahmanian. "Principles of Multimedia Database Systems" Morgan Kaufmann, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. Zhong and S.F. Chang. Video Object Model and Segmentation for Content-Based Video Indexing. IEEE Intern. Conf. on Circuits and Systems, June, 1997, Hong Kong.Google ScholarGoogle Scholar
  13. W. Zhou, A. Vellaikal, and C.C. Jay Kuo. Rule-Based Video Classification System for Basketball Video Indexing. Proc. ACM Multimedia Workshop, 2000, pp. 213--216. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. The CPR model for summarizing video

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      MMDB '03: Proceedings of the 1st ACM international workshop on Multimedia databases
      November 2003
      102 pages
      ISBN:1581137265
      DOI:10.1145/951676

      Copyright © 2003 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 November 2003

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • Article

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader