Color-Based Image Retrieval from High-Similarity Image Databases

  • Michael Edberg Hansen
  • Jens Michael Carstensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

Many image classification problems can fruitfully be thought of as image retrieval in a “high similarity image database” (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita (JM) distances between distributions of color (and color derivatives) estimated from a set of automatically extracted image regions. The weight coefficients are estimated based on optimal retrieval performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition.

Keywords

Image Retrieval Image Database Retrieval Performance Fungal Coloni Convex Linear Combination 
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.

References

  1. 1.
    D. Androutsos, K.N. Plataniotis, and A.N. Venetsanopoulos. Distance measures for color image retrieval. IEEE, 1998.Google Scholar
  2. 2.
    C. M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press-Oxford, 1995. ISBN: 0-19-853864-2.Google Scholar
  3. 3.
    L. Cinque, S. Levialdi, K.A. Olsen, and A. Pellicanó. Color-based image retrieval using spatial-chromatic histograms. IEEE, 1999.Google Scholar
  4. 4.
    Y. Deng, B.S. Manjunath, C. Kenney, M. S. Moore, and H. Shin. An efficient color representation for image retrieval. IEEE Transactions on Image Processing, 10(1):140–147, 2001.MATHCrossRefGoogle Scholar
  5. 5.
    K. Fukanaga. Introduction to Pattern Recognition. Academic Press, Inc., 2nd edition, 1990.Google Scholar
  6. 6.
    M. E. Hansen, F. Lund, and J. M. Carstensen. Visual clone identification of penicillium commune isolates. Journal of Microbiological Methods, 52(2):221–229, february 2003.CrossRefGoogle Scholar
  7. 7.
    T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning; datamining, inference and prediction. Springer, 2002.Google Scholar
  8. 8.
    http://www.videometer.com.Google Scholar
  9. 9.
    A. K. Jain and A. Vailaya. Image retrieval using color and shape. Pattern Recognition, 29(8):1233–1244, 1996.CrossRefGoogle Scholar
  10. 10.
    M. S. Kankanhalli, B. M. Mehtre, and H. Y. Huang. Color and spatial feature for content-based image retrieval. Pattern Recognition Letters, (20):109–118, 1999.MATHCrossRefGoogle Scholar
  11. 11.
    M. Mirmehdi and M. Petrou. Segmentation of color textures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(2):142–159, February 2000.CrossRefGoogle Scholar
  12. 12.
    J.I. Pitt. The genus penicillium and its teleomorphic states eupenicillium and talaromyces. Academic Press, London, 1979.Google Scholar
  13. 13.
    K.B. Raper and C. Thom. Manual of the penicillia. Williams and Wilkins, Baltimore, 1949.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Michael Edberg Hansen
    • 1
  • Jens Michael Carstensen
    • 1
  1. 1.Image Processing and Computer VisionInformatics and Mathematical ModellingLyngbyDenmark

Personalised recommendations