On Comparison of Clustering Techniques for Histogram PDF Estimation 1
Elmehdi Aitnouri, shuliang Wang, Djemel Ziou · 2000
This paper discusses the problem of finding the number of component clusters in gray-level image histograms. These histograms are often modeled using a standard mixture of univariate normal densities. The problem, however, is that the number of components in the mixture is an unknown variable that must be esti- mated, together with the means and the variances. Computing the number of components in a mixture usually requires unsupervised learning. This problem is denoted as validation in the cluster analysis litera- ture. The aim is to identify sub-populations believed to be present in a population. A wide variety of methods have been proposed for this purpose. In this paper, we intend to compare two methods, each belonging to a typ- ical approach. The first, somewhat classical method, is based on criterion optimization. We are particularly interested in the Akaike's information criterion. The second method is based on a direct approach that makes use of a cluster's geometric properties. In this paper, we develop an algorithm to generate non-overlapped test vectors, allowing the generation of a large set of verified vectors that can be used to perform objective evaluation and comparison.