Symbol Grounding for Semantic Image Interpretation: From Image Data to Semantics

This paper presents an original approach for the symbol grounding problem involved in semantic image interpretation, i.e. the problem of the mapping between image data and semantic data. Our approach involves the following aspects of cognitive vision : knowledge acquisition and knowledge representation, reasoning and machine learning. The symbol grounding problem is considered as a problem as such and we propose an independent cognitive system dedicated to symbol grounding. This symbol grounding system introduces an intermediate layer between the semantic interpretation problem (reasoning in the semantic level) and the image processing problem. An important aspect of the work concerns the use of two ontologies to make easier the communication between the different layers : a visual concept ontology and an image processing ontology. We use two approaches to solve the symbol grounding problem: a machine learning approach and an a priori knowledge based approach.