An evolutionary approach for ontology driven image interpretation

Authors:
Germain Forestier;Sébastien Derivaux;Cédric Wemmert;Pierre Gançarski
Affiliations:
LSIIT, CNRS, University Louis Pasteur, UMR, Illkirch, France;LSIIT, CNRS, University Louis Pasteur, UMR, Illkirch, France;LSIIT, CNRS, University Louis Pasteur, UMR, Illkirch, France;LSIIT, CNRS, University Louis Pasteur, UMR, Illkirch, France
Venue:
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Year:
2008

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Abstract

Image mining and interpretation is a quite complex process. In this article, we propose to model expert knowledge on objects present in an image through an ontology. This ontology will be used to drive a segmentation process by an evolutionary approach. This method uses a genetic algorithm to find segmentation parameters which allow to identify in the image the objects described by the expert in the ontology. The fitness function of the genetic algorithm uses the ontology to evaluate the segmentation. This approach does not needs examples and enables to reduce the semantic gap between automatic interpretation of images and expert knowledge.