Efficient Inference of Spatial Hierarchical Models

The long term goal of artificial intelligence and computer vision is to be able to build models of the world automatically and to use them for interpretation of new situations. It is natural that such models are efficiently organized in a hierarchical manner; a model is build by sub-models, these sub-models are again build of another models, and so on. These building blocks are usually shareable; different objects may consist of the same components. In this paper, we describe a hierarchical probabilistic model for visual domain and propose a method for its efficient inference based on data partitioning and dynamic programming. We show the behaviour of the model, which is in this case made manually, and inference method on a controlled yet challenging dataset consisting of rotated, scaled and occluded letters. The experiments show that the proposed model is robust to all above-mentioned aspects.