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Probabilistic Models for Compositional Hierarchies
Bernd Neumann
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
- 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.
Part of the compositional hierarchy used in eTRIMS
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