On the Interest of Spatial Relations and Fuzzy Representations for
Ontology-Based Image Interpretation

Isabelle Bloch, C´eline Hudelot¹ and Jamal Atif²

Ecole Nationale Sup´erieure des T´el´ecommunications – GET-T´el´ecom Paris

CNRS UMR 5141 LTCI – Signal and Image Processing Department

46 rue Barrault, 75013 Paris, France

¹Ecole Centrale Paris, Grande Voie des Vignes, 92 295 Chˆatenay-Malabry Cedex, France

²Universit´e des Antilles et de la Guyane, Guyane, France

Abstract

In this paper we highlight a few features of the semantic gap problem in image interpretation. We show that semantic image interpretation can be seen as a symbol grounding problem. In this context, ontologies provide a powerful framework to represent domain knowledge, concepts and their relations, and to reason about them. They are likely to be more and more developed for image interpretation. A lot of image interpretation systems rely strongly on descriptions of objects through their characteristics such as shape, location, image intensities. However, spatial relations are very important too and provide a structural description of the imaged phenomenon, which is often more stable and less prone to variability than pure object descriptions. We show that spatial relations can be integrated in domain ontologies. Because of the intrinsic vagueness we have to cope with, at different levels (image objects, spatial relations, variability, questions to be answered, etc.), fuzzy representations are well adapted and provide a consistent formal framework to address this key issue, as well as the associated reasoning and decision making aspects. Our view is that ontology-based methods can be very useful for image interpretation if they are associated to operational models relating the ontology concepts to image information. In particular, we propose operational models of spatial relations, based on fuzzy representations.