Serialized k-Means for Adaptative Color Image Segmentation

Application to Document Images and Others
  • Yann Leydier
  • Frank Le Bourgeois
  • Hubert Emptoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3163)


This paper introduces an adaptative segmentation system that was designed for color document image analysis. The method is based on the serialization of a k-means algorithm that is applied sequentially by using a sliding window over the image. During the window’s displacement, the algorithm reuses information from the clusters computed in the previous window and automatically adjusts them in order to adapt the classifier to any new local variation of the colors. To improve the results, we propose to define several different clusters in the color feature space for each logical class. We also reintroduce the user into the initialization step to define the number of classes and the different samples for each class. This method has been tested successfully on ancient color manuscripts, video images and multiple natural and non-natural images having heavy defects and showing illumination variation and transparency. The proposed algorithm is generic enough to be applied on a large variety of images for different purposes such as color image segmentation as well as binarization.


  1. 1.
    Le Bourgeois, F., et al.: Document Images Analysis solutions for Digital libraries. In: First International Workshop on Document Image Analysis for Libraries (DIAL), Palo Alto (2004)Google Scholar
  2. 2.
    Puzicha, J., Belongie, S.: Model-based halftoning for color image segmentation. In: International Conference on Pattern Recognition (ICPR), pp. 3633–3637 (2000)Google Scholar
  3. 3.
    Todoran, L., Worring, M.: Segmentation of Color Document Images, ISIS technical report series, vol. 21 (2000)Google Scholar
  4. 4.
    Lambert, P., Grecu, H.: A quick and coarse color image segmentation. In: International Conference on Image Processing (ICIP), pp. 965–968 (2003)Google Scholar
  5. 5.
    Zhang, C., Wang, P.: A new method of color image segmentation based on intensity and hue clustering. In: International Conference on Pattern Recognition (ICPR), pp. 3617–3621 (2000)Google Scholar
  6. 6.
    Wesolkowski, S., Tominaga, S., Dony, R.D.: Shading and Highlight Invariant Color Image Segmentation. In: SPIE, vol. 4300, pp. 229–240 (2001)Google Scholar
  7. 7.
    Comaniciu, D., Meer, P.: Robust Analysis of Feature Spaces: Color Image Segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, pp. 750–755 (1997)Google Scholar
  8. 8.
    Eglin, V., Bres, S.: Analysis and interpretation of visual saliency for document functional labeling. International Journal of Document Analysis and Recognition, IJDAR (2004) (to be published)Google Scholar
  9. 9.
    Sauvola, J., Seppänen, T., Haapakoski, S., Pietikäinen, M.: Adaptive Document Binarization. In: International Conference on Document Analysis and Recognition (ICDAR), Ulm, vol. 1, pp. 147–152 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yann Leydier
    • 1
    • 2
  • Frank Le Bourgeois
    • 2
  • Hubert Emptoz
    • 2
  1. 1.Archimed LyonLyonFrance
  2. 2.LIRISVilleurbanneFrance

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