Visual Scenes Clustering Using Variational Incremental Learning of Infinite Generalized Dirichlet Mixture Models

In this paper, we develop a clustering approach based on variational incremental learning of a Dirichlet process of generalized Dirichlet (GD) distributions. Our approach is built on nonparametric Bayesian analysis where the determination of the complexity of the mixture model (i.e. the number of components) is sidestepped by assuming an infinite number of mixture components. By leveraging an incremental variational inference algorithm, the model complexity and all the involved model’s parameters are estimated simultaneously and effectively in a single optimization framework. Moreover, thanks to its incremental nature and Bayesian roots, the proposed framework allows to avoid over- and under-fitting problems, and to offer good generalization capabilities. The effectiveness of the proposed approach is tested on a challenging application involving visual scenes clustering.