Advertisement

Evaluating the Performance of Content-Based Image Retrieval Systems

  • Markus Koskela
  • Jorma Laaksonen
  • Sami Laakso
  • Erkki Oja
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)

Abstract

Content-based image retrieval (CBIR) is a new but in recent years widely-adopted method for finding images from vast and unannotated image databases. CBIR is a technique for querying images on the basis of automatically-derived features such as color, texture, and shape directly from the visual content of images. For the development of effective image retrieval applications, one of the most urgent issues is to have widely-accepted performance assessment methods for different features and approaches. In this paper, we present methods for evaluating the retrieval performance of different features and existing CBIR systems. In addition, we present a set of retrieval performance experiments carried out with an experimental image retrieval system and a large database of images from a widely-available commercial image collection.

Keywords

Feature Vector Image Retrieval Relevance Feedback Feature Extraction Method Retrieval Performance 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Sami Brandt, Jorma Laaksonen, and Erkki Oja. Statistical shape features in content-based image retrieval. In Proceedings of 15th International Conference on Pattern Recognition, Barcelona, Spain, September 2000. To appear.Google Scholar
  2. [2]
    N.-S. Chang and K.-S. Fu. Query by pictorial example. IEEE Transactions on Software Engineering, 6(6):519–524, November 1980.CrossRefGoogle Scholar
  3. [3]
    The Corel Corporation WWW home page,http://www.corel.com, 1999.
  4. [4]
    Ingemar J. Cox, Matt L. Miller, Stephen M. Omohundro, and Peter N. Yianilos. Target testing and the PicHunter bayesian multimedia retrieval system. In Advanced Digital Libraries ADL’96 Forum, Washington, DC, May 1996.Google Scholar
  5. [5]
    Alexander Dimai. Assessment of effectiveness of content based image retrieval systems. In Third International Conference on Visual Information Systems, pages 525–532, Amsterdam, The Netherlands, June 1999.Google Scholar
  6. [6]
    Teuvo Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer-Verlag, Berlin, 1997. Second Extended Edition.Google Scholar
  7. [7]
    P. Koikkalainen and E. Oja. Self-organizing hierarchical feature maps. In Proc. IJCNN-90, Int. Joint Conf. on Neural Networks, Washington, DC, volume II, pages 279–285, Piscataway, NJ, 1990. IEEE Service Center.Google Scholar
  8. [8]
    Jorma Laaksonen, Markus Koskela, and Erkki Oja. Content-based image retrieval using self-organizing maps. In Third International Conference on Visual Information Systems, pages 541–548, Amsterdam, The Netherlands, June 1999.Google Scholar
  9. [10]
    Erkki Oja, Jorma Laaksonen, Markus Koskela, and Sami Brandt. Self-organizing maps for content-based image retrieval. In Erkki Oja and Samuel Kaski, editors, Kohonen Maps, pages 349–362. Elsevier, 1999.Google Scholar
  10. [11]
    G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. Computer Science Series. McGraw-Hill, 1983.Google Scholar
  11. [12]
    Markus Stricker and Markus Orengo. Similarity of color images. In Storage and Retrieval for Image and Video Databases III (SPIE), volume 2420 of SPIE Proceedings Series, pages 381–392, San Jose, CA, USA, February 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Markus Koskela
    • 1
  • Jorma Laaksonen
    • 1
  • Sami Laakso
    • 1
  • Erkki Oja
    • 1
  1. 1.Laboratory of Computer and Information ScienceHelsinki University of TechnologyHUTFinland

Personalised recommendations