Enhancing Semantic Image Retrieval by Query Expansion using User-Specific Annotation Profiles

J. Vompras and S. Conrad (Germany)


Information Retrieval, Image Retrieval, Semantic Annota tion, Information Access, Digital Libraries


The assignment of annotations still forms the basis for con ceptual queries in large image/text repositories by facili tating data classification at semantic level. However, the varying users’ perception of image contents and the usage of different retrieval aspects make it necessary to develop methods for the unification and integration of different an notation schemes. In this paper we present the IKONA Retrieval and Annotation System with its main focus on the transformation of the subjective annotations assigned by different users into a unified knowledge base. For that pur pose, the conducted queries are adjusted to user-dependent preferences by finding correspondences between the used vocabulary and the system’s ’core’ annotation ontology. The introduced method is evaluated on a large collection of news data including both images and the corresponding textual data. The experiments show that our approach sig nificantly increases the retrieval quality, particularly when users are faced with a data repository whose content is un known and has not been made completely semantically ac cessible.

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