Advertisement

A Modular Approach for Automating Video Analysis

  • Gayathri Nadarajan
  • Arnaud Renouf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

Automating the steps involved in video processing has yet to be tackled with much success by vision developers and knowledge engineers. This is due to the difficulty in formulating vision problems and their solutions in a generalised manner. In this collaborated work, we introduce a modular approach that utilises ontologies to capture the goals, domain description and capabilities for performing video analysis. This modularisation is tested on real-world videos from an ecological source and proves useful in conceptualising and generalising video processing tasks. On a more significant note, this could be used in a framework for automatic video analysis in emerging infrastructures such as the Grid.

Keywords

Knowledge-Based Vision Ontological Engineering Automatic Video Analysis Ontology-Based Systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gomez-Perez, A., Fernandez-Lopez, M., Corcho, O.: Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web (1st edn.) (2004)Google Scholar
  2. 2.
    Nouvel, A., Dalle, P.: An Interactive Approach For Image Ontology Definition. In: 13ème Congrès de Reconnaissance des Formes et Intelligence Artificielle, Angers, France, pp. 1023–1031 (2002)Google Scholar
  3. 3.
    Maillot, N., Thonnat, M., Boucher, A.: Towards Ontology Based Cognitive Vision (Long Version). Machine Vision and Applications 16(1), 33–40 (2004)CrossRefGoogle Scholar
  4. 4.
    Bombardier, V., Lhoste, P., Mazaud, C.: Modélisation et intégration de connaissances métier pour l’identification de défauts par règles linguistiques floues. Traitement du Signal 21(3), 227–247 (2004)Google Scholar
  5. 5.
    Hudelot, C.: Towards a Cognitive Vision Platform for Semantic Image Interpretation. Application to the Recognition of Biological Organisms. PhD thesis, Nice-Sophia Antipolis University (2005)Google Scholar
  6. 6.
    Town, C.: Ontological Inference for Image and Video Analysis. Mach. Vision Appl. 17(2), 94–115 (2006)CrossRefGoogle Scholar
  7. 7.
    McGuinness, D., van Harmelen, F.: OWL Web Ontology Language. World Wide Web Consortium (W3C) (2004), http://www.w3.org/TR/owl-features/
  8. 8.
    Matsuyama, T.: Expert Systems for Image Processing: Knowledge-Based Composition of Image Analysis Processes. CVGIP 48(1), 22–49 (1989)MathSciNetGoogle Scholar
  9. 9.
    Renouf, A., Clouard, R., Revenu, M.: How to Formulate Image Processing Applications? In: Proceedings of the International Conference on Computer Vision Systems, Bielefeld, Germany (2007)Google Scholar
  10. 10.
    EcoGrid National Center for High Performance Computing, Taiwan. http://ecogrid.nchc.org.tw/
  11. 11.
    Nadarajan, G., Chen-Burger, Y.H., Malone, J.: Semantic-Based Workflow Composition for Video Processing in the Grid. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 161–165 (2006)Google Scholar
  12. 12.
    Foster, I.: The Grid 2 – Blueprint for a New Computing Infrastructure, 2nd edn. Morgan Kaufmann, San Francisco (2004)Google Scholar
  13. 13.
    Liedtke, C., Blömer, A.: Architecture of the Knowledge Based Configuration System for Image Analysis ”Conny”. In: ICPR 1992, pp. 375–378 (1992)Google Scholar
  14. 14.
    Clément, V., Thonnat, M.: A Knowledge-Based Approach to Integration of Image Procedures Processing. CVGIP: Image Understanding 57(2), 166–184 (1993)CrossRefGoogle Scholar
  15. 15.
    Chien, S., Mortensen, H.: Automating Image Processing for Scientific Data Analysis of a large Image Database. IEEE PAMI 18(8), 854–859 (1996)Google Scholar
  16. 16.
    Clouard, R., Elmoataz, A., Porquet, C., Revenu, M.: Borg: A Knowledge-Based System for Automatic Generation of Image Processing Programs. IEEE PAMI 21(2), 128–144 (1999)Google Scholar
  17. 17.
    Draper, B., Hanson, A., Riseman, E.: Knowledge-directed vision: Control, learning, and integration. Proc. of IEEE 84, 1625–1681 (1996)CrossRefGoogle Scholar
  18. 18.
    Bloehdorn, S., Petridis, K., Saathoff, C., Simou, N., Tzouvaras, V., Avrithis, Y., Handschuh, S., Kompatsiaris, Y., Staab, S., Strintzis, M.G.: Semantic Annotation of Images and Videos for Multimedia Analysis. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 592–607. Springer, Heidelberg (2005)Google Scholar
  19. 19.
    Renouf, A.: (Hermès - a human-machine interface for the formulation of image processing applications), http://www.greyc.ensicaen.fr/~arenouf/Hermes

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gayathri Nadarajan
    • 1
  • Arnaud Renouf
    • 2
  1. 1.CISA, School of Informatics, University of EdinburghScotland
  2. 2.GREYC Laboratory (CNRS UMR 6072), Caen cedexFrance

Personalised recommendations