Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Information Retrieval
Modern Information Retrieval
Ontology-Based Photo Annotation
IEEE Intelligent Systems
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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In this paper, we present an approach for incorporating contextual knowledge into a multiuser image retrieval system which is based on annotations. Although the most existing keyword-based systems are expanded by conceptual knowledge (e.g. ontologies) modeling the topics in which the user is interested in, there still remain some unresolved problems, like existing differences in interpretation of image contents or inconsistencies in keyword assignments among different users. In our approach, multiple sources of information which are modeled as different annotation ontologies are brought together in order to extract contextual information, and thus attenuate users' subjectivity in content description. Finally, we evaluate our introduced approach on a real data set of sports images. The experiments show that our approach provides considerable retrieval quality, already in the first search iteration, which makes an additional query refinement dispensable. The results can even be further improved by applying lexical analysis for strings and error elimination methods.