Version Space Learning of Spatial Structures

Conceptual descriptions of 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 aggregate concepts for spatially related objects can be learnt from positive and negative examples. Our approach, based on Version Space Learning introduced by Mitchell [1], features a rich representation language encompassing quantitative and qualitative attributes and relations. Using examples from the buildings domain, we show that aggregate concepts for window arrays, balconies and other structures can in fact be learnt from annotated images and successfully employed in the conceptual knowledge base of a scene interpretation system [2].

Examples for learning of "balcony" and "window array" concept

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