Probabilistic models for compositional hierarchies

In the scene interpretation system SCENIC, high-level knowledge about visual scenes is currently represented by means of a logic-based knowledge representation language, using taxonomical and compositional hierarchies. We are developing a probabilistic framework which can be combined with our hierarchical knowledge structures. It will support probabilistic learning methods for high-level structures such as building facades, and provide probabilistic guidance for stepwise scene interpretation. By imposing intuitive abstraction properties on compositional hierarchies, evidence propagation during the interpretation process may become computationally feasible even in large knowledge bases.