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Semi-Automatic Semantic Video Annotation Tool

Conference paper

Abstract

Video management systems require semantic annotation of video for indexing and retrieval tasks. Currently, it is not possible to extract all high-level semantic information automatically. Also, automatic content based retrieval systems use high-level semantic annotations as ground truth data. We present a semi-automatic semantic video annotation tool that assists user to generate annotations to describe video in fine detail. Annotation process is partly automated to reduce annotation time. Generated annotations are in MPEG-7 metadata format for interoperability.

Keywords

Video annotation MPEG-7 Semi-automatic annotation 

References

  1. 1.
    MPEG-7: ISO/IEC 15938. Multimedia Content Description Interface (2001)Google Scholar
  2. 2.
    Bailer, W., Schallauer, P.: The detailed audiovisual profile: enabling interoperability between MPEG-7 based systems. In: Proceedings of the 12th International MultiMedia Modelling Conference, pp. 217–224 (2006)Google Scholar
  3. 3.
    Dasiopoulou, S., Giannakidou, E., Litos, G., Malasioti, P., Kompatsiaris, Y.: A survey of semantic image and video annotation tools. In: Knowledge-Driven Multimedia Information Extraction and Ontology Evolution, pp. 196–239 (2011)Google Scholar
  4. 4.
    Heggland, J.: Ontolog: temporal annotation using ad hoc ontologies and application profiles. In: Research and Advanced Technology for Digital Libraries, pp. 5–17 (2002)Google Scholar
  5. 5.
    Lin, C., Tseng, B., Smith, J.: VideoAnnEx: IBM MPEG-7 annotation tool for multimedia indexing and concept learning. In: IEEE International Conference on Multimedia and Expo (2003)Google Scholar
  6. 6.
    Schallauer, P., Ober, S., Neuschmied, H.: Efficient semantic video annotation by object and shot re-detection. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) Posters and Demos Session, 2nd International Conference on Semantic and Digital Media Technologies (SAMT). Koblenz, Germany (2008)Google Scholar
  7. 7.
    Lowe D.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE, Los Alamitos (1999)Google Scholar
  8. 8.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Computer Vision-ECCV, pp. 404–417 (2006)Google Scholar
  9. 9.
    M. R. Group: Reference software. In: ISO/IEC JTC1/SC29 15938–6 (2003)Google Scholar
  10. 10.
    Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying production effects. Multimedia Syst. 7(2), 119–128 (1999)CrossRefGoogle Scholar
  11. 11.
    Bradski, G.: Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. Q2, 1–15 (1998)Google Scholar
  12. 12.
    Zen, H., Hasegawa, T., Ozawa, S.: Moving object detection from MPEG coded picture. In: Proceedings of International Conference on Image Processing (ICIP), vol. 4, pp. 25–29. IEEE, Los Alamitos (1999)Google Scholar
  13. 13.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  14. 14.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: DAGM 25th Pattern Recognition, Symposium, pp. 297–304 (2003)Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

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