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OLYBIA: Ontology-Based Automatic Image Annotation System Using Semantic Inference Rules

OLYBIA: Ontology-Based Automatic Image Annotation System Using Semantic Inference Rules,10.1007/978-3-540-71703-4_42,Kyung-wook Park,Jin-woo Jeong,Don

OLYBIA: Ontology-Based Automatic Image Annotation System Using Semantic Inference Rules   (Citations: 3)
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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.
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