Bayesian Compositional Hierarchies - A Probabilistic Structure for Scene Interpretation

In high-level vision, it is often useful to organize conceptual models in compositional hierarchies. For example, models of building facades (which are used here as examples) can be described in terms of constituent parts such as balconies or window arrays which in turn may be further decomposed. While compositional hierarchies are widely used in scene interpretation, it is not clear how to model and exploit probabilistic dependencies which may exist within and between aggregates. In this contribution I present Bayesian Aggregate Hierarchies as a means to capture probabilistic dependencies in a compositional hierarchy. The formalism integrates well with object-centered representations and extends Bayesian Networks by allowing arbitrary probabilistic dependencies within aggregates. To obtain efficient inference procedures, the aggregate structure must possess abstraction properties which ensure that internal aggregate properties are only affected in accordance with the hierarchical structure. Using examples from the building domain, it is shown that probabilistic aggregate information can thus be integrated into a logic-based scene interpretation system and provide a preference measure for interpretation steps

Formalization of conceptual classification models of knowledge

The paper explores the information (conceptual) systems in terms of the hierarchies of conceptual and material systems. Such systems are an objectoriented from the system-object approach side. The conceptual systems of which are external systems (systems-classes) that determine the properties of specific objects and objects are material or internal systems (facts systems) that implement real interactions. The paper also considers the results of a formal description of the hierarchy of conceptual systems. It takes into account their systemic relationships using a description logic for formalizing certain states of the system-object approac