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Erschienen in:
2005 | OriginalPaper | Buchkapitel
A Full-Text Framework for the Image Retrieval Signal/Semantic Integration
verfasst von : Mohammed Belkhatir, Philippe Mulhem, Yves Chiaramella
Erschienen in: Database and Expert Systems Applications
Verlag: Springer Berlin Heidelberg
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This paper presents an approach for integrating perceptual signal features (i.e. color and texture) and semantic information within a coupled architecture for image indexing and retrieval. It relies on an expressive knowledge representation formalism handling high-level image descriptions and a full-text query framework. It consequently brings the level of image retrieval closer to users’ needs by translating low-level signal features to high-level conceptual data and integrate them with semantic characterization within index and query structures. Experiments on a corpus of 2500 photographs validate our approach by considering recall-precision indicators over a set of 46 full-text queries coupling high-level semantic and signal features.