Abstract
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.
Postdoc Research Fellow, FWO, Belgium.
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References
A.K. Jain and R.C. Dubes: Algorithms for Clustering Data. Prentice Hall, 1988.
Leonard Kaufman and Peter J. Rousseeuw: Finding Groups in Data: An Introduction to Cluster Analysis. J. Wiley and Sons, 1990.
W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos and G. Taubin
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.
R.W. Picard: A Society of Models for Video and Image Libraries. M.I.T. Media Lab Technical Report No.
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.
J.R. Thompson and R.A. Tapia: Nonparametric Function Estimation, Modeling and Simulation. SIAM, 1990.
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© 1997 Springer-Verlag Berlin Heidelberg
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Pauwels, E., Fiddelaers, P., Mindru, F. (1997). Fully unsupervised clustering using centre-surround receptive fields with applications to colour-segmentation. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_95
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DOI: https://doi.org/10.1007/3-540-63460-6_95
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