Automatic Audiovisual Documents Genre Description

Manel Fourati, Anis Jedidi, Faiez Gargouri

Abstract

Audiovisual documents are among the most proliferated resources. Faced with these huge quantities produced every day, the lack of significant descriptions without missing the important content arises. The extraction of these descriptions requires an analysis of the audiovisual document’s content. The automation of the process of describing audiovisual documents is essential because of the richness and the diversity of the available analytical criteria. In this paper, we present a method that allows the extraction of a semantic and automatic description from the content such as genre. We chose to describe cinematic audiovisual documents based on the documentation prepared in the pre-production phase of films, namely synopsis. The experimental result on Imdb (Internet Movie Database) and the Wikipedia encyclopedia indicate that our method of genre detection is better than the result of these corpuses.

References

  1. Brezealed, B and Cook, D. 2006. Using closed captions and visual features to classify movies by genre. In Proceedings of Multimedia Data Mining/Knowledge Discovery and Data Mining. MDM 7807, New York, NY, USA, Article No. 4.
  2. Brett, K., Geoffrey, N., Hinrich, S. 1997. “Automatic Detection of Text Genre”. ACL'97, Madrid, pages 32- 38.
  3. Chernoff, H., 1954. On the distribution of the likelihood ratio. Ann. Math. Stat. 25:573-578. Journal,Volume 25, Number 3 (1954), 573-578.
  4. C. Van Rijsbergen. 1979. Information Retrieval (Book 2nd ed). Butterworth-Heinemann Newton, MA, USA, 1979.
  5. E.Stamatatos, N.Fakotakis, G. Kokkinakis, “Text Genre Detection Using Common Word Frequencies”, In Proc. of the 18th Int. Conf. on Computational Linguistics (COLING2000) Saarbrücken, Germany, pp. 808-814, 2000
  6. Hyoyoung, K., Jin, Wan Park. , Genre Visualization Based on Words Used in Text. International Conference, HCI International 2013, Las Vegas, NV, USA, July, 2013, Proceedings, Part II .pp 551-554.
  7. Internet Movie Database, Available at: http://www.imdb. com/ last visit:18/07/2014.
  8. Jaccard p. 1901.Etude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la société vaudoise des sciences naturelles, vol 37, pp 547-5790.
  9. Karlgren, J and Douglass,C. 1994. Recognizing text genres with simple metrics using discriminant analysis. In Proceedings of Coling 94, Kyoto, Japan, pp 1071-1075.
  10. Karlgren, J., Ivan, B., Johan, D., Anders, H., Niklas, W., Iterative Information Retrieval Using Fast Clustering and Usage-Specific Genres”, 8th DELOS workshop on User Interfaces in Digital Libraries, Stockholm, Sweden, 21-23 October 1998. Pages 85-92.
  11. Lin Wei-Hao and Alexander Hauptmann. 2002. News video classification using SVM-based multimodal classifiers and combination strategies. In Proceedings of the tenth ACM international conference on Multimedia. ACM, New York, NY, USA, 323-326.
  12. LR Lawlor, Overlap, similarity, and competition coefficients. Journal, Vol. 61, No. 2, Apr., 1980.Pages 245-251.
  13. Marc, N., Jocelyne N., Peter, R. King and Ludovic, G. 2007.Genre Driven Multimedia Document Production by means of Incremental Transformation. In proceedings of the 2007 ACM symposium on Document engineering. ACM, New York, NY, USA, 111-120.
  14. Medelyan, O., Witten I. H. (2006) "Thesaurus Based Automatic Keyphrase Indexing." In Proc. of the Joint Conference on Digital Libraries 2006, Chapel Hill, NC, USA, pp. 296-297.
  15. Nei, M., Li, WH. 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA 76: 5269-5273.
  16. Ronan Cummins. 2013. A Standard Document Score for Information Retrieval. In Proceedings of the 2013 Conference on the Theory of Information Retrieval, Oren Kurland, Donald Metzler, Christina Lioma, Birger Larsen, and Peter Ingwersen (Eds.). ACM, New York, NY, USA, Pages 24, 4 pages.
  17. Stanislas, O., Mickael, R. and Georges, L. Transcriptionbased video genre classification. IEEE. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 14-19 March 2010. Dallas, TX. Pages 5114 - 5117.
  18. Stefano, C., Alessandro, B., Marco, B., Emanuele, DV., Piero F., Silvia, Q. 2013. An Introduction to Information Retrieval. Book Web Information Retrieval, 2013. pp 3-11.
  19. Yong-Bae Lee and Sung Hyon Myaeng (2002). Text Genre Classification with Genre-Revealing and Subject-Revealing Features. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002. ACM, New York, NY, USA. Pages 145-150.
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Paper Citation


in Harvard Style

Fourati M., Jedidi A. and Gargouri F. (2014). Automatic Audiovisual Documents Genre Description . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014) ISBN 978-989-758-048-2, pages 538-543. DOI: 10.5220/0005170905380543


in Bibtex Style

@conference{sstm14,
author={Manel Fourati and Anis Jedidi and Faiez Gargouri},
title={Automatic Audiovisual Documents Genre Description},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014)},
year={2014},
pages={538-543},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005170905380543},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014)
TI - Automatic Audiovisual Documents Genre Description
SN - 978-989-758-048-2
AU - Fourati M.
AU - Jedidi A.
AU - Gargouri F.
PY - 2014
SP - 538
EP - 543
DO - 10.5220/0005170905380543