Image database categorisation using robust modelling of finite generalised Dirichlet mixture

We propose a novel image database categorisation approach using Robust Modelling of finite Generalised Dirichlet Mixture (RM-GDM). The proposed algorithm is based on optimising an objective function that associates two types of memberships with each data sample. The first one is the posterior probability and indicates how well a sample fits each estimated distribution. The second membership represents the degree of typicality and is used to identify and discard noise points and outliers. These properties make RM-GDM suitable for noisy and high-dimensional feature spaces. We use the RM-GDM to categorise a large collection of colour images. Its performance is illustrated and compared to similar algorithms.