Abstract
State of the art image tagging systems are limited because they allow users to annotate image tags in noun form, which cannot fully express the semantics of image content. In this paper, we propose Linked Tag, a semi-automatic image annotation system that inserts semantic relationships between tags. The proposed annotation method connects image tags using predicate words that can capture the contexts in which the image tags are used. In particular, we exploit Linked Data such as DBPedia in order to connect the image tags with a property value. Compared with tag-based annotation and ontology-based annotation systems, Linked Tag eliminates a large amount of manual labor and enhances the semantic expression of image content. We also introduce two annotation-based applications on Linked Tag. First, we propose SPARQL query processing for image retrieval, which enables us to express visual appearance as well as semantic information. Second, we propose a novel tag-ranking algorithm based on the link analysis in the RDF annotation graph. Finally, we demonstrate the operation of our proposed system and analyze its efficacy.
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