Skip to main content

Domain Knowledge Extension with Pictorially Enriched Ontologies

  • Conference paper
Book cover Computer Analysis of Images and Patterns (CAIP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3691))

Included in the following conference series:

Abstract

Classifying video elements according to some pre-defined ontology of the video content is the typical way to perform video annotation. Ontologies are built by defining relationship between linguistic terms that describe domain concepts at different abstraction levels. Linguistic terms are appropriate to distinguish specific events and object categories but they are inadequate when they must describe video entities or specific patterns of events. In these cases visual prototypes can better express pattern specifications and the diversity of visual events. To support video annotation up to the level of pattern specification enriched ontologies, that include visual concepts together with linguistic keywords, are needed. This paper presents Pictorially Enriched ontologies and provides a solution for their implementation in the soccer video domain. The pictorially enriched ontology created is used both to directly assign multimedia objects to concepts, providing a more meaningful definition than the linguistics terms, and to extend the initial knowledge of the domain, adding subclasses of highlights or new highlight classes that were not defined in the linguistic ontology. Automatic annotation of soccer clips up to the pattern specification level using a pictorially enriched ontology is discussed.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. World Wide Web Consortium, “Resource description framework (rdf),” Tech. Rep., W3C (Febraury 2004), http://www.w3.org/RDF/

  2. Leonardi, R., Migliorati, P.: Semantic indexing of multimedia documents. IEEE Multimedia 9(2), 44–51 (2002)

    Article  Google Scholar 

  3. Ekin, A., Murat Tekalp, A., Mehrotra, R.: Automatic soccer video analysis and summarization. IEEE Transactions on Image Processing 12(7), 796–807 (2003)

    Article  Google Scholar 

  4. Assfalg, J., Bertini, M., Colombo, C., Del Bimbo, A., Nunziati, W.: Semantic annotation of soccer videos: automatic highlights identification. Computer Vision and Image Understanding 92(2-3), 285–305 (2003)

    Article  Google Scholar 

  5. Yu, X., Xu, C., Leung, H.W., 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, vol. 3, pp. 11–20 (2003)

    Google Scholar 

  6. 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 

  7. Mezaris, V., Kompatsiaris, I., Boulgouris, N.V., Strintzis, M.G.: 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)

    Article  Google Scholar 

  8. Jaimes, A., Tseng, B., Smith, J.R.: 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, pp. 145–150. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Jaimes, A., Smith, J.R.: Semi-automatic, data-driven construction of multimedia ontologies. In: Proc. of IEEE Int’l Conference on Multimedia & Expo. (2003)

    Google Scholar 

  10. Benitez, A.B., Chang, S.-F.: Automatic multimedia knowledge discovery, summarization and evaluation. IEEE Transactions on Multimedia (2003) (Submitted)

    Google Scholar 

  11. Strintzis, M.G., Bloehdorn, S., Handschuh, S., Staab, S., Simou, N., Tzouvaras, V., Petridis, K., Kompatsiaris, I., Avrithis, Y.: Knowledge representation for semantic multimedia content analysis and reasoning. In: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (November 2004)

    Google Scholar 

  12. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bertini, M., Cucchiara, R., Del Bimbo, A., Torniai, C. (2005). Domain Knowledge Extension with Pictorially Enriched Ontologies. In: Gagalowicz, A., Philips, W. (eds) Computer Analysis of Images and Patterns. CAIP 2005. Lecture Notes in Computer Science, vol 3691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556121_80

Download citation

  • DOI: https://doi.org/10.1007/11556121_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28969-2

  • Online ISBN: 978-3-540-32011-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics