Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Computational Media Aesthetics

  • Chitra Dorai
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1036

Synonyms

CMA; Media semantics; Production-based approach to media analysis

Definition

Computational media aesthetics is defined as the algorithmic study of a variety of image and aural elements in media founded on their patterns of use in film grammar, and the computational analysis of the principles that have emerged underlying their manipulation, individually or jointly, in the creative art of clarifying, intensifying, and interpreting some event for the audience [3]. It is a computational framework to establish semantic relationships between the various elements of sight, sound, and motion in the depicted content of a video and to enable deriving reliable, high-level concept-oriented content annotations as opposed to verbose low-level features computed today in video processing for search and retrieval, and nonlinear browsing of video. This media production knowledge-guided semantic analysis has led to a shift away from a focus on low level features that cannot answer high level...

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

Recommended Reading

  1. 1.
    Adams B, Dorai C, Venkatesh S. Towards automatic extraction of expressive elements from motion pictures: tempo. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2000. p. 641–45.Google Scholar
  2. 2.
    Arijon D. Grammar of the film language. Los Angeles: Silman-James Press; 1976.Google Scholar
  3. 3.
    Dorai C, Venkatesh S. Computational media aesthetics: finding meaning beautiful. IEEE Multimedia. 2001;8(4):10–2.CrossRefGoogle Scholar
  4. 4.
    Davis M. Editing out video editing. IEEE Multimedia. 2003;10(2):2–12.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Mulhem P, Kankanhalli MS, Ji Yi, Hassan H. Pivot vector space approach for audio-video mixing. IEEE Multimedia. 2003;10(2):28–40.CrossRefGoogle Scholar
  6. 6.
    Salway A, Graham M. Extracting information about emotions in films. In: Proceedings of the 9th International Conference on Multimedia Modeling; 2003. p. 299–302.Google Scholar
  7. 7.
    Sarvas R, Herrarte E, Wilhelm A, Davis M. Metadata creation system for mobile images. In: Proceedings of the 2nd International Conference Mobile Systems, Applications and Services; 2004. p. 36–48.Google Scholar
  8. 8.
    Smeulders A, Worring M, Santini S, Gupta A. Content based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell. 2000;22(12):1349–80.CrossRefGoogle Scholar
  9. 9.
    Yazhong Feng, Yueting Zhuang, Yunhe Pan. Music information retrieval by detecting mood via computational media aesthetics. In: Proceedings of the IEEE/WIC International Conference on Web Intelligence; 2003. p. 235–41.Google Scholar
  10. 10.
    Zettl H. Sight, sound, motion: applied media aesthetics. Belmont: Wadsworth Publishing; 1999.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBM T. J. Watson Research CenterHawthorneUSA

Section editors and affiliations

  • Vincent Oria
    • 1
  • Shin'ichi Satoh
    • 2
  1. 1.Dept. of Computer ScienceNew Jersey Inst. of TechnologyNewarkUSA
  2. 2.Digital Content and Media Sciences ReseaMultimedia Information Research DivisionNational Institute of InformaticsTokyoJapan