An Expandable Hierarchical Statistical Framework for Count Data Modeling and Its Application to Object Classification

The problem that we address in this paper is that of learning hierarchical object categories. Indeed, Digital media technology generates huge amount of non-textual information. Categorizing this information is a challenging task which has served important applications. An important part of this nontextual information is composed of images and videos which consists of various objects each of which may be used to effectively classify the images or videos. Object classification in computer vision can be looked upon from several different perspectives. From the structural perspective object classification models can be divided into flat and hierarchical models. Many of the well-known hierarchical structures proposed so far are based on the Dirichlet distribution. In this work, however, we present a generative hierarchical statistical model based on generalized Dirichlet distribution for the categorization of visual objects modeled as a set of local features describing patches detected using interest points detector. We demonstrate the effectiveness of the proposed model through extensive experiments.