Dynamic Image Segmentation Method Using Hierarchical Clustering

  • Jorge Galbiati
  • Héctor Allende
  • Carlos Becerra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


In this paper we explore the use of the cluster analysis in segmentation problems, that is, identifying image points with an indication of the region or class they belong to. The proposed algorithm uses the well known agglomerative hierarchical cluster analysis algorithm in order to form clusters of pixels, but modified so as to cope with the high dimensionality of the problem. The results of different stages of the algorithm are saved, thus retaining a collection of segmented images ordered by degree of segmentation. This allows the user to view the whole collection and choose the one that suits him best for his particular application.


Segmentation analysis growing region clustering methods 


  1. 1.
    Allende, H., Galbiati, J.: A non-parametric filter for digital image restoration, using cluster analysis. Pattern Recognition Letters 25, 841–847 (2004)CrossRefGoogle Scholar
  2. 2.
    Allende, H., Galbiati, J.: Edge detection in contaminated images, using cluster analysis. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 945–953. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Allende, H., Becerra, C., Galbiati, J.: A segmentation method for digital images based on cluster analysis. Lecture Notes In Computer Science: Advanced And Natural Computing Algorithms, vol. 4431(1), pp. 554–563 (2007)Google Scholar
  4. 4.
    Frucci, M., Ramella, G., di Baja, G.S.: Using resolution pyramids for watershed image segmentation. Image and Vision Computing 25(6), 1021 (2007)CrossRefGoogle Scholar
  5. 5.
    Fukui, M., Kato, N., Ikeda, H., Kashimura, H.: Size-independent image segmentation by hierarchical clustering and its application for face detection. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 686–693. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Martin, D., Fowlkes, C., Tal, D., Malikand, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring. In: Ecological Statistics, Proc. 8th Int’l Conf. on Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  7. 7.
    Martinez-Uso, A., Pla, F., Garcia-Sevilla, P.: Unsupervised image segmentation using a hierarchical clustering selection process. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 799–807. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Pan, Z., Lu, J.: A Bayes-based region-growing algorithm for medical image segmentation. Computing In Science and Engineering 9(4), 32–38 (2007)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Pauwels, J., Frederix, G.: Finding salient regions in images. Computer Vision and Image Understyanding 75, 73–85 (1999)CrossRefGoogle Scholar
  10. 10.
    Wang, K.B., Yu, B.Z., Zhao, J., Li, H.N., Xie, H.M.: Texture image segmentation based on Gabor wavelet using active contours without edges. Department of Electronic Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jorge Galbiati
    • 1
  • Héctor Allende
    • 2
    • 3
  • Carlos Becerra
    • 4
  1. 1.Department of StatisticsPontificia Universidad Católica de ValparaísoChile
  2. 2.Department of InformaticsUniversidad Técnica Federico Santa MaríaChile
  3. 3.Science and Ingeneering FacultyUniversidad Adolfo IbáñezChile
  4. 4.Department of Computer ScienceUniversidad de ValparaísoChile

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