Fully unsupervised clustering using centre-surround receptive fields with applications to colour-segmentation

  • Eric Pauwels
  • Peter Fiddelaers
  • Florica Mindru
Pattern Analysis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)


In this paper we argue that the emphasis on similarity-matching within the context of Content-based Image Retrieval (CBIR) highlights the need for improved and reliable clustering-algorithms. We propose a fully unsupervised clustering algorithm that is obtained by changing the non-parametric density estimation problem in two ways. Firstly, we use cross-validation to select the appropriate width of the convolution-kernel. Secondly, using kernels with a positive centre and a negative surround (DOGS) allows for a better discrimination between clusters and frees us from having to choose an arbitrary cut-off thresh- old. No assumption about the underlying data-distribution is necessary and the algorithm can be applied in spaces of arbitrary dimension. As an illustration we have applied the algorithm to colour-segmentation problems.


Receptive Field Retinal Ganglion Cell Positive Centre Neurophysiological Data Unsupervised Cluster Algorithm 
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.
    A.K. Jain and R.C. Dubes: Algorithms for Clustering Data. Prentice Hall, 1988.Google Scholar
  2. 2.
    Leonard Kaufman and Peter J. Rousseeuw: Finding Groups in Data: An Introduction to Cluster Analysis. J. Wiley and Sons, 1990.Google Scholar
  3. 3.
    W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos and G. TaubinGoogle Scholar
  4. 4.
    A. Pentland, R.W. Picard and S. Sclaroff: Photobook: Content-Based Manipulation of Image Databases. SPIE Storage and Retrieval Image and Video Databases II, No. 2185, Feb 6–10, 1994, San Jose.Google Scholar
  5. 5.
    R.W. Picard: A Society of Models for Video and Image Libraries. M.I.T. Media Lab Technical Report No.Google Scholar
  6. 6.
    K. Popat and R. W. Picard: Cluster-Based Probability Model and Its Application to Image and Texture Processing. IEEE Trans. on Image Processing, Vol.6, No.2, Feb. 1997, pp. 268–284.CrossRefGoogle Scholar
  7. 7.
    J.R. Thompson and R.A. Tapia: Nonparametric Function Estimation, Modeling and Simulation. SIAM, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Eric Pauwels
    • 1
  • Peter Fiddelaers
    • 1
  • Florica Mindru
    • 1
  1. 1.ESAT-VISICS, K.U.LeuvenLeuvenBelgium

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