Evaluation of Color Image Segmentation Algorithms Based on Histogram Thresholding

  • Patrick Ndjiki-Nya
  • Ghislain Simo
  • Thomas Wiegand
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3893)


Image segmentation is an essential processing step in texture analysis systems, as its accuracy has a significant impact on the quality of the final analysis result. The downside of texture analysis is that segmentation is one of the most difficult tasks in image processing. In this paper, algorithms for improved color image segmentation are presented. They are all based on a histogram thresholding approach that was developed for monochrome images for it has proven to be very effective. Improvements over the genuine segmentation approach are measured and the best optimization algorithm is determined.


Receiver Operating Characteristic Curve Image Segmentation Segmentation Approach Texture Region Monochrome Image 
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.


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  1. 1.
    Wilson, R., Spann, M.: Image Segmentation and Uncertainty. Pattern Recognition and Image Processing Series. Research Studies Press Ltd, England (1988)Google Scholar
  2. 2.
    Freixenet, J., et al.: Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. In: Proc. 7th European Conference on Computer Vision-Part III, pp. 408–422 (2002)Google Scholar
  3. 3.
    Huang, Q., Dom, B.: Quantitative Methods of Evaluating Image Segmentation. In: Proc. ICIP, vol. 3, pp. 53–56 (1995)Google Scholar
  4. 4.
    Wilson, R., Spann, M.: Finite Prolate Spheroidal Sequences and their Applications II: Image Feature Description and Segmentation. IEEE Trans. on PAMI 10, 193–203 (1988)CrossRefGoogle Scholar
  5. 5.
    Spann, M., Wilson, R.: A Quad-tree Approach to Image Segmentation which Combines Statistical and Spatial Information. Pattern Recognition 18(3/4), 257–269 (1985)CrossRefGoogle Scholar
  6. 6.
    Wilson, R., Knutsson, H., Granlund, G.H.: The Operational Definition of the Position of Line and Edge. In: Proc. ICPR (1982)Google Scholar
  7. 7.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color Image Segmentation: Advances and Prospects. Pattern Recognition 34, 2259–2281 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Littmann, E., Ritter, H.: Adaptive Color Segmentation – A Comparison of Neural and Statistical Methods. IEEE Trans. on Neural Networks 8(1), 175–185 (1997)CrossRefGoogle Scholar
  9. 9.
    Ohta, Y., Kanade, T., Sakai, T.: Color Information for Region Segmentation. Computer Graphics and Image Processing 13(3), 222–241 (1980)CrossRefGoogle Scholar
  10. 10.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  11. 11.
    Hanley, J.A., Mc Neil, B.J.: The Meaning and Use of the Area under the Receiver Operating Characteristic (ROC) Curve. Radiology 1(143), 29–36 (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Patrick Ndjiki-Nya
    • 1
  • Ghislain Simo
    • 1
  • Thomas Wiegand
    • 1
  1. 1.Image Communication Group, Image Processing DepartmentFraunhofer Heinrich-Hertz-InstitutBerlinGermany

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