Generation of Rules from Ontologies for High-Level Scene Interpretation

Abstract

Abstract. In this paper, a novel architecture for high-level scene interpretation is introduced, which is based on the generation of rules from an OWL-DL ontology. It is shown that the object-centered structure of the ontology can be transformed into a rule-based system in a native and systematic way. Furthermore the integration of constraints -which are essential for scene interpretation -is demonstrated with a temporal constraint net, and it is shown how parallel computing of alternatives can be realised. First results are given using examples of airport activities.

Describing Images Using Qualitative Models and Description Logics

“…Only a few approaches, using DL-based ontologies to enhance highlevel image interpretation, can be found in the literature (Maillot & Thonnat, 2008;Johnston et al, 2008;Schill et al, 2009;Bohlken & Neumann, 2009). Maillot and Thonnat (2008) describe images using an ontology that contains qualitative features of shape, colour, texture, size and topology and apply this description to the classification of pollen grains.…”

OVIS: ontology video surveillance indexing and retrieval system

“…Nevertheless, this approach has some limitations about mainstream ontology and its application in video analysis. Bohlken et al [23] considered the problem of high-level scene interpretation suggesting a novel architecture based on the generation of rules from an OWL-DL ontology. However, the concept of vehicle entering a zone is not conceptual, because it represents an action between vehicles and zone concepts.…”

Qualitative distances and qualitative description of images for indoor scene description and recognition in robotics

“…Only a few approaches, using DL-based ontologies to enhance high-level image interpretation, can be found in the literature [Maillot and Thonnat, 2008;Johnston et al, 2008;Schill et al, 2009;Bohlken and Neumann, 2009]. Maillot and Thonnat [2008] describe images using an ontology that contains qualitative features of shape, colour, texture, size and topology and apply this description to the classification of pollen grains.…”

“…This approach could be considered as complementary to ours, and future extensions may consider the introduction of uncertainty. Bohlken and Neumann [2009] present a novel approach in which a DL ontology is combined with the use of rules to improve the definition of constraints for scene interpretation. The use of rules enables them to combine the open world semantics of DLs with closed world constraint validation.…”