Abstract
One of the big issues facing current content-based image retrieval is how to automatically extract the high-level concepts from images. In this paper, we present an efficient system that automatically extracts the high-level concepts from images by using ontologies and semantic inference rules. In our method, MPEG-7 visual descriptors are used to extract the visual features of image, and the visual features are mapped to semi-concepts via the mapping algorithm. We also build the visual and animal ontologies to bridge the semantic gap. The visual ontology allows the definition of relationships among the classes describing the visual features and has the values of semi-concepts as the property values. The animal ontology can be exploited to identify the high-level concept in an image. Also, the semantic inference rules are applied to the ontologies to extract the high-level concept. Finally, we evaluate the proposed system using the image data set including various animal objects and discuss the limitations of our system.
This work was supported by Korea Research Foundation Grant funded by the Korea Government(MOEHRD) (KRF-2006-521-D00457).
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Park, KW., Jeong, JW., Lee, DH. (2007). OLYBIA: Ontology-Based Automatic Image Annotation System Using Semantic Inference Rules. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_42
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DOI: https://doi.org/10.1007/978-3-540-71703-4_42
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