Video Semantic Content Analysis Framework Based on Ontology Combined MPEG-7

  • Liang Bai
  • Songyang Lao
  • Weiming Zhang
  • Gareth J. F. Jones
  • Alan F. Smeaton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4918)


The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results.


Video Semantic Content MPEG-7 Ontology OWL Description Logic Temporal Description Logic 


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  1. 1.
    Chang, S.-F.: The holy grail of content-based media analysis. IEEE Multimedia 9(2), 6–10 (2002)CrossRefGoogle Scholar
  2. 2.
    Yoshitaka, A., Ichikawa, T.: A survey on content-based retrieval for multimedia databases. IEEE Transactions on Knowledge and Data Engineering 11(1), 81–93 (1999)CrossRefGoogle Scholar
  3. 3.
    Hanjalic, A., Xu, L.Q.: Affective video content representation and modeling. IEEE Transactions on Multimedia 7(1), 143–154 (2005)CrossRefGoogle Scholar
  4. 4.
    Muller-Schneiders, S., Jager, T., Loos, H.S., Niem, W.: Performance evaluation of a real time video surveillance system. In: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, October 15-16, 2005, pp. 137–143 (2005)Google Scholar
  5. 5.
    Hua, X.S., Lu, L., Zhang, H.J.: Automatic music video generation based on the temporal pattern analysis. In: 12th annual ACM international conference on Multimedia (October 2004)Google Scholar
  6. 6.
    Resource description framework. Technical report, W3C (February 2004),
  7. 7.
    Web ontology language (OWL). Technical report, W3C (2004),
  8. 8.
    Ekin, A., Tekalp, A.M., Mehrotra, R.: Automatic soccer video analysis and summarization. IEEE Transactions on Image Processing 12(7), 796–807 (2003)CrossRefGoogle Scholar
  9. 9.
    Yu, X., Xu, C., Leung, H., Tian, Q., Tang, Q., Wan, K.W.: Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video. In: ACM Multimedia 2003, Berkeley, CA(USA), November 4-6, 2003, vol. 3, pp. 11–20 (2003)Google Scholar
  10. 10.
    Xu, H.X., Chua, T.-S.: Fusion of AV features and external information sources for event detection in team sports video. ACM transactions on Multimedia Computing, Communications and Applications 2(1), 44–67 (2006)CrossRefGoogle Scholar
  11. 11.
    Reidsma, D., Kuper, J., Declerck, T., Saggion, H., Cunningham, H.: Cross document ontology based information extraction for multimedia retrieval. In: Supplementary proceedings of the ICCS 2003, Dresden (July 2003)Google Scholar
  12. 12.
    Mezaris, V., Kompatsiaris, I., Boulgouris, N., Strintzis, M.: Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval. IEEE Transactions on Circuits and Systems for Video Technology 14(5), 606–621 (2004)CrossRefGoogle Scholar
  13. 13.
    Jaimes, A., Tseng, B., Smith, J.: Modal keywords, ontologies, and reasoning for video understanding. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Jaimes, A., Smith, J.: Semi-automatic, data-driven construction of multimedia ontologies. In: Proc. of IEEE Int’l Conference on Multimedia & Expo (2003)Google Scholar
  15. 15.
    Bertini, M., Bimbo, A.D., Torniai, C.: Enhanced ontoloies for video annotation and retrieval. In: ACM MIR’2005, Singapore, November 10-11 (2005)Google Scholar
  16. 16.
    Bentitez, A., Chang, S.-F.: Automatic multimedia knowledge discovery, summarization and evaluation. IEEE Transactions on Multimedia (submitted, 2003)Google Scholar
  17. 17.
    Dasiopoulou, S., Papastathis, V.K., Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: An Ontology Framework for Knowledge-Assisted Semantic Video Analysis and Annotation. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Kompatsiaris, I., Mezaris, V., Strintzis, M.G.: Multimedia content indexing and retrieval using an object ontology. In: Stamou, G. (ed.) Multimedia Content and Semantic Web Methods, Standards and Tools. Wiley, New York (2004)Google Scholar
  19. 19.
    MPEG-7 Overview (October 2004),
  20. 20.
    TV-Anytime Forum,
  21. 21.
    MPEG-21 Multimedia Framework,
  22. 22.
  23. 23.
    Artale, A., Franconi, E.: A temporal description logic for reasoning about actions and plans. Journal of Artificial Intelligence Research 9, 463–506 (1998)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Chen, J.Y., Li, Y.H., Lao, S.Y., et al.: Detection of Scoring Event in Soccer Video for Highlight Generation. Technical Report, National University of Defense Technology (2004)Google Scholar
  25. 25.
    Pan, H., van Beek, P., Sezan, M.I.: Detection of Slow-motion Replay Segments in Sports Video for Highlights Generation. In: Proceedings of IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2001), Salt Lake City, UT, USA (May 2001)Google Scholar
  26. 26.
    Liang, B., Yanli, H., Songyang, L., Jianyun, C., Lingda, W.: Feature Analysis and Extraction for Audio Automatic Classification. In: IEEE SMC 2005, Hawaii USA, October 10-12 (2005)Google Scholar
  27. 27.
    Zhou, W., Dao, S., Jay Kuo, C.-C.: On-line knowledge and rule-based video classification system for video indexing and dissemination. Information Systems 27(8), 559–586 (2002)zbMATHCrossRefGoogle Scholar
  28. 28.
    Lao, S.Y., Smeaton, A.F., Jones, G.J.F., Lee, H.: A Query Description Model Based on Basic Semantic Unit Composite Petri-Nets for Soccer Video Analysis. In: Proceedings of ACM MIR 2004, New York, USA, October 15-16 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Liang Bai
    • 1
    • 2
  • Songyang Lao
    • 1
  • Weiming Zhang
    • 1
  • Gareth J. F. Jones
    • 2
  • Alan F. Smeaton
    • 2
  1. 1.School of Information System & ManagementNational University of Defense TechnologyChangShaChina
  2. 2.Centre for Digital Video ProcessingDublin City University, GlasnevinDublin 9Ireland

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