A statistically principled approach to 1-dimensional clustering was introduced by Pauwels abd Frederix (2000). In this approach clustering is achieved by finding the smoothest density that is statistically compatible with the observed data. In the current contribution we propose two solutions for the optimisation problem that is at the heart of this algorithm. The first solution is based on spline-functions, while the second hinges on an expansion of the density in terms of Gaussians. The latter is reminiscent of mixture-models but fundamentally different in its interpretation. Finally, we argue that 1-dimensional histogram segmentation yields a powerful local nonparametric cluster-validity criterion that can be used to check the quality of proposed clusterings in higher dimensions.

We outline the architecture of a content-based image retrieval (CBIR)-interface that offers the user a graphical tool to create new features by showing (as opposed to telling!) the system what he means. It allows him to interactively classify images by dragging and dropping them into different piles and instructing the interface to come up with features that can mimic this classification. We show how logistic regression and Sammon projection can be used to supervise this search mode.

In cluster-based segmentation pixels are mapped into various feature spaces whereupon they are subjected to a grouping algorithm. In this paper we develop a robust and versatile non-parametric clustering algorithm that is able to handle the unbalanced and irregular clusters encountered in such segmentation applications. The strength of our approach lies in the definition and use of two cluster validity indices that are independent of the cluster topology. By combining them, an excellent clustering can be identified, and experiments confirm that the associated clusters do indeed correspond to perceptually salient image regions.