Abstract
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.
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Belkhatir, M., Mulhem, P., Chiaramella, Y. (2005). A Full-Text Framework for the Image Retrieval Signal/Semantic Integration. In: Andersen, K.V., Debenham, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2005. Lecture Notes in Computer Science, vol 3588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546924_12
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DOI: https://doi.org/10.1007/11546924_12
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