Unsupervised Image Segmentation Using a Hierarchical Clustering Selection Process

  • Adolfo Martínez-Usó
  • Filiberto Pla
  • Pedro García-Sevilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


In this paper we present an unsupervised algorithm to select the most adequate grouping of regions in an image using a hierarchical clustering scheme. Then, we introduce an optimisation approach for the whole process. The grouping method presented is based on the maximisation of a measure that represents the perceptual decision. The whole strategy takes profit from a hierarchical clustering to find a maximum of the proposed criterion. The algorithm has been used to segment real images as well as multispectral images achieving very accurate results on this task.


Image Segmentation Segmentation Result Active Contour Criterion Function Multispectral 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.


  1. 1.
    Borsotti, M., Campadelli, P., Schettini, R.: Quantitative evaluation of color image segmentation results. Pattern Recognition Letters 19, 741–747 (1998)MATHCrossRefGoogle Scholar
  2. 2.
    Buhmann, J.: Data clustering and learning, The Handbook of Brain Theory and Neural Networks, 2nd edn., pp. 308–312 (2002)Google Scholar
  3. 3.
    Cheng, H.-D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: Advances and prospects. Pattern Recognition 34(12), 2259–2281 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Gurari, E.M., Wechsler, H.: On the difficulties involved in the segmentation of pictures. PAMI(4) (3), 304–306 (1982)Google Scholar
  5. 5.
    Haralick, R.H., Shapiro, L.G.: Image segmentation techniques. Computer Vision, Graphics, and Image Processing (29), 100–132 (1985)CrossRefGoogle Scholar
  6. 6.
    Keuchel, J., Heiler, M., Schnörr, C.: Hierarchical image segmentation based on semidefinite programming. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 120–128. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Makrogiannis, S., Economou, G., Fotopoulos, S., Nikolaos, G.B.: Segmentation of color images using multiscale clustering and graph theoretic region synthesis. IEEE Transactions on Systems, Man, and Cybernetics 35(2), 224–238 (2005)CrossRefGoogle Scholar
  8. 8.
    Muñoz, X., Freixenet, J., Cufí, X., Martí, J.: Strategies for image segmentation combining region and boundary information. PRL 24(1-3), 375–392 (2003)Google Scholar
  9. 9.
    Nixon, M., Aguado, A.S.: Feature Extraction in Computer Vision and Image Processing (2002)Google Scholar
  10. 10.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  11. 11.
    Pauwels, E.J., Frederix, G.: Finding salient regions in images: Non-parametric clustering for image segmentation and grouping. Computer Vision and Image Understanding 75(1/2), 73–85 (1999)CrossRefGoogle Scholar
  12. 12.
    Samet, H.: Applications of Spatial Data Structures: Computer Graphics, Image Processing and GIS (1990)Google Scholar
  13. 13.
    Zhang, Y.J.: A review of recent evaluation methods for image segmentation. In: Proceedings of the Sixth ISSPA, vol. 1, pp. 148–151 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Adolfo Martínez-Usó
    • 1
  • Filiberto Pla
    • 1
  • Pedro García-Sevilla
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosJaume I Univerisity 

Personalised recommendations