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Formulating Film Tempo

The Computational Media Aesthetics Methodology in Practice

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Book cover Media Computing

Part of the book series: The Springer International Series in Video Computing ((VICO,volume 4))

Abstract

This chapter constitutes a detailed example of Computational Media Aesthetics at work. A short history of approaches to the problems posed by automatic content management in its broadest context is presented, cast in the light of their ability to obtain the much needed semantic grid with which to interpret their object. Our consideration is then further narrowed to the video medium, where we identify two common categories of solution to the problem, the kitchen sink and the brittle mapping, note their relative weaknesses, and show them to be directly attributable to the nature of the semantic grid chosen, or lack thereof. Focusing on our domain of Film, we argue that the best semantic grid for its interpretation is that within which its creators work; namely film grammar. In order to demonstrate this, we develop a measure for the extraction of a fundamental aspect of film, Tempo. From definition, to formulation, and even its exploitation resulting in the location of high-level filmic components such as dramatic occurrences, the process is guided by film grammar at every step. Example results are provided from the movie, The Matrix.

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References

  1. Adams, B., Dorai, C., and Venkatesh, S. Novel approach to determining movie tempo and dramatic story sections in motion pictures. In 2000 International Conference on Image Processing, (September 2000), vol. II, pp. 283–286.

    Google Scholar 

  2. Adams, B., Dorai, C., and Venkatesh, S. Role of shot length in characterizing tempo and dramatic story sections in motion pictures. In IEEE Pacific Rim Conference on Multimedia 2000 (December 2000), pp. 54–57.

    Google Scholar 

  3. Adams, B., Dorai, C., and Venkatesh, S. Study of shot length and motion as contributing factors to movie tempo. In 8th ACM International Conference on Multimedia (November 2000), pp. 353–355.

    Chapter  Google Scholar 

  4. Adams, B., Dorai, C., and Venkatesh, S. Towards automatic extraction of expressive elements from motion pictures: Tempo. In IEEE International Conference on Multimedia and Expo (July 2000), vol. II, pp. 641–645.

    Google Scholar 

  5. Adjeroh, D., Lee, M., and King, I. A distance measure for video sequences. Computer Vision and Image Understanding 75, 1/2 (July 1999), 25–45.

    Article  Google Scholar 

  6. Aigrain, P., Joly, P., and Longueville, V. Medium knowledge-based macro-segmentation of video into sequences. In Mark Maybury, editor, Proc. IJCAI Workshop on Intelligent Multimedia Information Retrieval (Montreal, August 1995), vol. 12.

    Google Scholar 

  7. Allen, J.F. Maintaining knowledge about temporal intervals. Communications of the Association for Computing Machinery 26, 11 (1983), 832–843.

    Article  MATH  Google Scholar 

  8. Arijon, D. Grammar of the Film Language. Silman-James Press, 1976.

    Google Scholar 

  9. Block, R.A. Cognitive Models of Psychological Time. Lawrence Erlbaum Associates, Publishers, 1990.

    Google Scholar 

  10. Brandt, M. Traditional film editing vs. electronic nonlinear film editing: A comparison of feature films, http://www.nonlinear3.com/brandt.htm, 1998.

    Google Scholar 

  11. Deriche, R. Recursively implementing the Gaussian and its derivatives. In ICIP’92, Proc. 2nd Singapore Int. Conf. on Image Processing (1992), pp. 263–267.

    Google Scholar 

  12. Dorai, C. and Venkatesh, S. Computational Media Aesthetics: Finding meaning beautiful. IEEE Multimedia 8, 4 (October–December 2001), 10–12.

    Article  Google Scholar 

  13. Doulamis, A., Avrithis, Y., Doulamis, N., and Kollias, S. Interactive content-based retrieval in video databases using fuzzy classification and relevance feedback. In ICMCS’97 (1999).

    Google Scholar 

  14. Fischer, S., Lienhart, R., and Effelsberg, W. Automatic recognition of film genres. Tech. rep., University of Mannheim, Germany, 1995.

    Google Scholar 

  15. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., and Yanker, P. Query by image and video content: The QBIC system. In Intelligent multimedia information retrieval, M. Maybury, Ed. MIT Press, 1997, pp. 7–22.

    Google Scholar 

  16. [16] Goldman, W. Adventures in the Screen Trade: A Personal View of Hollywood. Abacus, 2000.

    Google Scholar 

  17. Hanjalic, A., Lagendijk, R., and Biemond, J. Automatically segmenting movies into logical story units. In Visual Information and Information Systems (1999), pp. 229–236.

    Chapter  Google Scholar 

  18. Hirsch, E.D. Jr. Validity in Interpretation. Yale University Press, 1967.

    Google Scholar 

  19. Li, Y., Ming, W., and Kuo, C.-C. Semantic video content abstraction based on multiple cues. In IEEE International Conference on Multimedia and Expo 2001, ICME2001 (Tokyo, Japan, August 2001), pp. 804–807.

    Google Scholar 

  20. Lienhart, R., Pfeiffer, S., and Effelsberg, W. Video abstracting. Communications of the ACM 40, 12 (1997), 54–63.

    Article  Google Scholar 

  21. Mahdi, W., Chen, L., and Fontaine, D. Improving the spatial-temporal clue based segmentation by the use of rhythm. In Second European Conference, ECDL’98 (1998).

    Google Scholar 

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

    Google Scholar 

  23. Mediaware Solutions Pty Ltd. Mediaware solutions WebFlix Pro vl.5.3. http://www.mediaware.com.au/webflix.html, 1999.

    Google Scholar 

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

    Google Scholar 

  25. Metz, C. Film Language: A Semiotics of the Cinema. Oxford University Press, 1974.

    Google Scholar 

  26. Mitchell, T.M. Machine Learning. New York, McGraw-Hill, 1997.

    MATH  Google Scholar 

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

    Google Scholar 

  28. Naphade, M. and Huang, T.S. A probabilistic framework for semantic video indexing, filtering, and retrieval. IEEE Trans. Multimedia 3, 1 (2001), 141–151.

    Article  Google Scholar 

  29. Pentland, A., Picard, R., and Sclaroff, S. Photobook: Tools for content-based manipulation of image databases. In SPIE, Storage and Retrieval of Image and Video Databases II (1994), pp. 2185–05.

    Google Scholar 

  30. Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 1994.

    Google Scholar 

  31. Salt, B. Film Style and Technology: History and Analysis. Starword, London, 1992.

    Google Scholar 

  32. Salvaggio, J.L. A Theory of Film Language. Arno Press, 1980.

    Google Scholar 

  33. Santini, S. Semantic modalities in content-based retrieval. In IEEE International Conference on Multimedia and Expo (New York City, USA, July 2000), vol. II.

    Google Scholar 

  34. Sobchack, T. and Sobchack, V. An Introduction to Film. Scot, Foresman and Company, 1987.

    Google Scholar 

  35. Srinivasan, M., Venkatesh, S., and Hosie, R. Qualitative extraction of camera parameters. Pattern Recognition 30, 4 (1997), 593–606.

    Article  Google Scholar 

  36. Truong, B.T., Dorai, C., and Venkatesh, S. New enhancements to cut, fade, and dissolve detection processes in video segmentation. In Proceedings of the 8th ACM International Conference on Multimedia (Los Angeles, California, November 2000), pp. 219–227.

    Google Scholar 

  37. Vasconcelos, N. and Lippman, A. A Bayesian video modeling framework for shot segmentation and content characterization. In CVPR’97 (San Juan, Puerto Rico, 1997).

    Google Scholar 

  38. Vasconcelos, N. and Lippman, A. Bayesian modeling of video editing and structure: Semantic features for video summarization and browsing. In ICIP’98 (Chicago, Illinois, 1998).

    Google Scholar 

  39. Vendrig, J., Worring, M., and Smeulders, A. Model based interactive story unit segmentation. In IEEE International Conference on Multimedia and Expo 2001, ICME2001 (Tokyo, Japan, August 2001), pp. 1084–1087.

    Google Scholar 

  40. Vogler, C. The Writer’s Journey: Mythic Structure for Storytellers and Screenwriters. Michael Wiese Productions, 1992.

    Google Scholar 

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

    Google Scholar 

  42. Yoshitaka, A., Ishii, T., Hirakawa, M., and Ichikawa, T. Content-based retrieval of video data by the grammar of film. In IEEE Symposium on Visual Languages (Capri, Italy, 1997).

    Google Scholar 

  43. Zettl, H. Sight, Sound, Motion: Applied Media Aesthetics. Wadsworth Pub Co., 1973.

    Google Scholar 

  44. Zhao, L., Yang, S.-Q., and Feng, B. Video scene detection using slide windows method based on temporal constraint shot similarity. In IEEE International Conference on Multimedia and Expo 2001, ICME2001 (Tokyo, Japan, August 2001), pp. 649–652.

    Google Scholar 

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Adams, B., Dorai, C., Venkatesh, S. (2002). Formulating Film Tempo. In: Dorai, C., Venkatesh, S. (eds) Media Computing. The Springer International Series in Video Computing, vol 4. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1119-9_4

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  • DOI: https://doi.org/10.1007/978-1-4615-1119-9_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5403-1

  • Online ISBN: 978-1-4615-1119-9

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