High-Level Expectations for Low-Level Image Processing
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
An Irregular Pyramid for Multi-scale Analysis of Objects and Their Parts
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Not far away from home: a relational distance-based approach to understanding images of houses
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
3D Scene interpretation by combining probability theory and logic: The tower of knowledge
Computer Vision and Image Understanding
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Ontological concept descriptions of scene objects and aggregates play an essential role in model-based scene interpretation. An aggregate specifies a set of objects with certain properties and relations which together constitute a meaningful scene entity. In this paper we show how ontological concept descriptions for spatially related objects and aggregates can be learnt from positive and negative examples. Our approach features a rich representation language encompassing quantitative and qualitative attributes and relations. Using examples from the buildings domain, we show that learnt aggregate concepts for window arrays, balconies and other structures can be successfully employed in the conceptual knowledge base of a scene interpretation system. Furthermore we argue that our approach can be extended to cover ontological concepts of any kind, with very few restrictions.