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.
[1]
Peter J. Rousseeuw,et al.
Finding Groups in Data: An Introduction to Cluster Analysis
,
1990
.
[2]
Kris Popat,et al.
Cluster-based probability model and its application to image and texture processing
,
1997,
IEEE Trans. Image Process..
[3]
Anil K. Jain,et al.
Algorithms for Clustering Data
,
1988
.
[4]
R. Tapia,et al.
Nonparametric Function Estimation, Modeling, and Simulation
,
1987
.
[5]
Rosalind W. Picard.
A Society of Models for Video and Image Libraries
,
1996,
IBM Syst. J..