Skip to main content
SpringerLink
Log in
Book cover

Joint Pattern Recognition Symposium

DAGM 2001: Pattern Recognition pp 9–16Cite as

SIMBA — Search Images by Appearance

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2191))

Abstract

In this paper we present SIMBA, a content based image retrieval system performing queries based on image appearance. We consider absolute object positions irrelevant for image similarity here and therefore propose to use invariant features. Based on a general construction method (integration over the transformation group), we derive invariant feature histograms that catch different cues of image content: features that are strongly influenced by color and textural features that are robust to illumination changes. By a weighted combination of these features the user can adapt the similarity measure according to his needs, thus improving the retrieval results considerably. The feature extraction does not require any manual interaction, so that it might be used for fully automatic annotation in heavily fluctuating image databases.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. H. Burkhardt and S. Siggelkow. Invariant features in pattern recognition-fundamentals and applications. In I. Pitas and C. Kotropoulos, editors, Nonlinear Model-Based Image/Video Processing and Analysis, pages 269–307. John Wiley & Sons, 2001.

    Google Scholar 

  2. M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: The QBIC system. IEEE Computer, 28(9):23–32, September 1995.

    Google Scholar 

  3. J. Li, J. Z. Wang, and G. Wiederhold. IRM: Integrated region matching for image retrieval. In Proceedings of the 2000 ACM Multimedia Conference, pages 147–156, Los Angeles, October 2000. ACM.

    Chapter  Google Scholar 

  4. F. Liu and R. W. Picard. Periodicity, directionality, and randomness:Wold features for image modeling and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7):722–733, July 1996.

    Article  Google Scholar 

  5. A. Lumini and D. Maio. Haruspex: an image database system for query-byexamples. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR) 2000, volume 4, pages 258–261, Barcelona, Spain, September 2000.

    Google Scholar 

  6. T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classifications based on feature distributions. Pattern Recognition, 29(1):51–59, 1996.

    Article  Google Scholar 

  7. G. Pass and R. Zabih. Histogram refinement for content-based image retrieval. In Proceedings of the 1996 Workshop on the Applications of Computer Vision, Sarasota, Florida, December 1996.

    Google Scholar 

  8. M. Schael and H. Burkhardt. Automatic detection of errors on textures using invariant grey scale features and polynomial classifiers. In M. K. Pietikäinen, editor, Texture Analysis in Machine Vision, volume 40 of Machine Perception and Artificial Intelligence, pages 219–230. World Scientific, 2000.

    Google Scholar 

  9. H. Schulz-Mirbach. Invariant features for gray scale images. In G. Sagerer, S. Posch, and F. Kummert, editors, Mustererkennung, DAGM 1995, pages 1–14, Bielefeld, 1995.

    Google Scholar 

  10. S. Siggelkow and H. Burkhardt. Invariant feature histograms for texture classification. In Proceedings of the 1998 Joint Conference on Information Sciences (JCIS), Research Triangle Park, North Carolina, USA, October 1998.

    Google Scholar 

  11. S. Siggelkow and H. Burkhardt. Fast invariant feature extraction for image retrieval. In H. Burkhardt, H.-P. Kriegel, and R. Veltkamp, editors, State-of-the-Art in Content-Based Image and Video Retrieval. Kluwer Academic Publishers, 2001. To appear.

    Google Scholar 

  12. S. Siggelkow and M. Schael. Fast estimation of invariant features. In W. Förstner, J. M. Buhmann, A. Faber, and P. Faber, editors, Mustererkennung, DAGM 1999, Informatik aktuell, pages 181–188, Bonn, September 1999.

    Google Scholar 

  13. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):1349–1380, December 2000.

    Article  Google Scholar 

  14. J. R. Smith and S.-F. Chang. Local color and texture extraction and spatial query. In Proceedings of the 1996 IEEE International Conference on Image Processing (ICIP’96), volume III, pages 1011–1014, Lausanne, Switzerland, September 1996.

    Google Scholar 

  15. M. Stricker and A. Dimai. Color indexing with weak spatial constraints. In Storage and Retrieval for Image and Video Databases IV, volume 2670 of SPIE Proceedings Series, pages 29–40, February 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Pattern Recognition and Image Processing, Computer Science Department, Albert-Ludwigs-Universität Freiburg, Germany

    Sven Siggelkow, Marc Schael & Hans Burkhardt

Authors
  1. Sven Siggelkow
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marc Schael
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hans Burkhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Chair of Image Understanding and Knowledge Based Systems, Technical University Munich, Orleansstr. 34, 81667, Munich, Germany

    Bernd Radig  & Stefan Florczyk  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Siggelkow, S., Schael, M., Burkhardt, H. (2001). SIMBA — Search Images by Appearance. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_2

Download citation

  • DOI: https://doi.org/10.1007/3-540-45404-7_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42596-0

  • Online ISBN: 978-3-540-45404-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation