Extracting Symbolic Descriptors for Interactive Object Retrieval

  • Jochen Wickel
  • Pablo Alvarado
  • Karl-Friedrich Kraiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


In visual object retrieval, there are always queries which cannot be processed successfully. In these cases, it is desirable to extract imprecise symbolic information. We present an architecture that extracts symbolic descriptors of the objects shown in the query image. The method is based on a combination of numeric feature extraction and classification. We describe some examples of descriptors and present first experimental results.


Image Processing Pattern Recognition Computer Vision Feature Extraction Computer Graphic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    M. Bariani, R. Cucchiara, M. Piccardi, and P. Mello. Data mining for automated visual inspection. In Proceedings of First Int. Conf. On Practical Application of. Knowledge Discovery and Data Mining (PADD’ 97), pages 51–64, 1997.Google Scholar
  2. [2]
    M. Bober. MPEG-7 visual shape descriptors. IEEE Transactions on Circuits and. Systems for Video Technology, 11(6):716–719, June 2001.CrossRefGoogle Scholar
  3. [3]
    C. Dillon and T. Caelli. Learning image annotation: The CITE system. Videre, 1(2):90–121, 1998.Google Scholar
  4. [4]
    B. Draper, R. Collins, J. Brolio, A. Hanson, and E. Riseman. The Schema system. Int. Journal of Computer Vision, 2(3):209–250, January 1989.CrossRefGoogle Scholar
  5. [5]
    S. Hollfelder, A. Everts, and U. Thiel. Concept-based browsing in video libraries. In Proc. of the IEEE Forum on Research and Technology Advances in Digital Libraries. (IEEE ADL 99), pages 105–115, 1999.Google Scholar
  6. [6]
    T. Krüger, J. Wickel, P. Alvarado, and K.-F. Kraiss. Feature extraction from VRML models for view-based object recognition. In Proc. of the 4th European. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2003), pages 391–394, 2003.Google Scholar
  7. [7]
    Y. Liu, W. E. Rothfus, and T. Kanade. Content-based 3d neuroradiologic image retrieval: Preliminary results. In IEEE Int. Workshop on Content-based Access of. Image and Video Databases, pages 91–100, Jan. 1998.Google Scholar
  8. [8]
    F. Mokhtarian, S. Abbasi, and J. Kittler. Robust and efficient shape indexing through curvature scale space. In Proc. of British Machine Vision Conf., pages 53–62, Edinburgh, UK, 1996.Google Scholar
  9. [9]
    J. Wickel, P. Alvarado, P. Dörfler, T. Krüger, and K.-F. Kraiss. Axiom — a modular visual object retrieval system. In M. Jarke, J. Koehler, and G. Lakemeyer, editors, KI 2002: Advances in Artificial Intelligence, pages 253–267. Springer, 2002.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jochen Wickel
    • 1
  • Pablo Alvarado
    • 1
  • Karl-Friedrich Kraiss
    • 1
  1. 1.Lehrstuhl für Technische InformatikRWTH AachenAachen

Personalised recommendations