IMAGE DATABASE CATEGORIZATION USING ROBUST UNSUPERVISED LEARNING OF FINITE GENERALIZED DIRICHLET MIXTURE MODELS

We propose a novel image database categorization approach using robust unsupervised learning of finite generalized dirichlet mixture models with feature discrimination. The proposed algorithm is based on optimizing 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. In addition, RULe_GDM learns an optimal relevance weight for each feature subset within each cluster. These properties make RULe_GDM suitable for noisy and high-dimensional feature spaces. We also extend our algorithm to find the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. RULe_GDM is used to categorize a collection of color images. The performance of RULe_GDM is illustrated and compared to similar algorithms.