Visual Scenes Categorization Using a Flexible Hierarchical Mixture Model Supporting Users Ontology

We introduce a novel hierarchical mixture model where each component is composed of a set of finite probability densities forming a super class mixture. Our proposed model can be viewed as a mixture of mixtures to support multi-level hierarchies where the structure of the hierarchy can be altered according to users´ ontological models within costless computational time. The proposed approach is generalized to adopt any probability density function and an algorithm to learn the model is proposed. In this paper, we adopt the inverted Dirichlet distribution to build the model, and a simulation study is performed to validate the proposed approach using synthetic and a real world challenging application concerning visual scenes categorization.