Image database categorization using robust modeling of finite Generalized Dirichlet mixture

Abstract

We propose a novel image database categorization approach using a possibilistic clustering algorithm. The proposed algorithm is based on a robust data modeling using the Generalized Dirichlet (GD) finite mixture and generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership represents the degree of “typicality” and is used to indentify and discard noise points. The algorithm minimizes one objective function to optimize GD mixture parameters and possibilistic membership values. This optimization is done iteratively by dynamically updating the density mixture parameters and the membership values in each iteration. The performance of the proposed algorithm is illustrated by using it to categorize a collection of 500 color images. The results are compared with those obtained by the Fuzzy C-means algorithm.