Skip to main content
SpringerLink
Log in

A Probabilistic Framework for Extracting Narrative Act Boundaries and Semantics in Motion Pictures

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This work constitutes the first attempt to extract the important narrative structure, the 3-Act storytelling paradigm in film. Widely prevalent in the domain of film, it forms the foundation and framework in which a film can be made to function as an effective tool for story telling, and its extraction is a vital step in automatic content management for film data. The identification of act boundaries allows for structuralizing film at a level far higher than existing segmentation frameworks, which include shot detection and scene identification, and provides a basis for inferences about the semantic content of dramatic events in film. A novel act boundary likelihood function for Act 1 and 2 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations and the results are reported for experiments involving 25 full-length movies. The result proves to be a useful tool in both the automatic and semi-interactive setting for semantic analysis of film, with potential application to analogues occuring in many other domains, including news, training video, sitcoms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. B. Adams, C. Dorai, and S. Venkatesh, “Towards automatic extraction of expressive elements from motion pictures: Tempo,” in IEEE International Conference on Multimedia and Expo, 2000, Vol. II. pp. 641–645.

    Google Scholar 

  2. B. Adams, C. Dorai, and S. Venkatesh, “Formulating film tempo-The computational media aesthetics methodology in practice,” in Media Computing: {Computational Media Aesthetics}, Kluwer Academic Publishers, 2002, pp. 57–84.

  3. D. Arijon, Grammar of the film language, Silman-James Press, 1976.

  4. L. Aronson, Scriptwriting Updated: New and Conventional Ways of Writing for the Screen, Allen and Unwin, 2000.

  5. C. Dorai and S. Venkatesh, “Computational media {Aesthetics}: Finding meaning beautiful,” IEEE Multimedia, Vol. 8, No. 4, pp. 10–12, 2001.

    Article  Google Scholar 

  6. C. Dorai and S. Venkatesh, “Bridging the Semantic Gap in Content Management Systems-Computational Media Aesthetics,” in Media Computing: {Computational Media Aesthetics}, Kluwer Academic Publishers, 2002.

  7. S. Field, Screenplay: The Foundations of Screenwriting, 3rd Ed, Dell Publishing, 1994.

  8. W. Goldman, Adventures in the Screen Trade: A Personal View of Hollywood. Abacus, 2000.

  9. IBM, “Made to Order: IBM makes sense of unstructured data,” 2002, http://www.research.ibm.com/thinkresearch/pages/2002/20020308_unstructured.shtml.

  10. A. Jain, R. Duin, and J. Mao, “Statistical pattern recognition: A review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 4–37, 2000.

    Article  Google Scholar 

  11. C. Keane, How To Write a Selling Screenplay: A Step by Step Approach to Developing Your Story and Writing Your Screenplay by One of Today’s Most Successful Screenwriters and Teachers, Broadway Books, 1998.

  12. Y. Li, W. Ming, and C.-C. Kuo, “Semantic video content abstraction based on multiple cues,” in IEEE International Conference on Multimedia and Expo 2001, ICME2001. Tokyo, Japan, 2001, pp. 804–807.

  13. R. Lienhart, S. Pfeiffer, and W. Effelsberg, “Scene determination based on video and audio features,” in EEE Multimedia Computing and Systems, ICMCS99, Florence, Italy, 1999.

  14. R. McKee, Story: Substance, Structure, Style, and the Principles of Screenwriting, ReganBooks, 1997.

  15. M. Mehring, The Screenplay: A Blend of Film Form and Content, Focal Press, 1990.

  16. J. Monaco, How to read a film: The Art, Technology, Language, History and Theory of Film and Media, Oxford University Press, 1981.

  17. M. Skov and P. Andersen, “Designing Interactive Narratives,” in COSIGN 2001.

  18. T. Sobchack and V. Sobchack, An introduction to film, Scot, Foresman and Company, 1987.

  19. K. Thompson, Storytelling in the New Hollywood: Understanding Classical Narrative Technique, Harvard University Press, 1999.

  20. C. Vogler, The Writer’s Journey: Mythic Structure for Storytellers and Screenwriters, Revised and Expanded, Pan Books, 1999.

  21. R. Winkler, Introduction to Bayesian Inference and Decision, Holt, Rinehart and Winston, Inc, 1972.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Curtin University of Technology, GPO Box U1987, Perth, 6845, W. Australia

    Brett Adams & Svetha Venketesh

  2. Department of Computer Science, Curtin University, Curtin

    Hung H. Bui

  3. IBM T. J. Watson Research Center, P.O. Box 704, Yorktown Heights, New York, 10598, USA

    Chitra Dorai

Authors
  1. Brett Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Svetha Venketesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hung H. Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chitra Dorai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brett Adams.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Adams, B., Venketesh, S., Bui, H.H. et al. A Probabilistic Framework for Extracting Narrative Act Boundaries and Semantics in Motion Pictures. Multimed Tools Appl 27, 195–213 (2005). https://doi.org/10.1007/s11042-005-2574-2

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-005-2574-2

Keywords

Search

Navigation