PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Meghini C. Faceted content-based image retrieval. In: DEXA 2008 - Workshop on Dynamic Taxonomies and Faceted Search. 19th International Conference on Database and Expert Systems Applications (Turin, 1-5 September 2008). Proceedings, pp. 402 - 406. IEEE, 2008.
 
 
Abstract
(English)
In typical content-based image retrieval systems it is not possible to navigate the image space by simultaneously applying multiple similarity criteria. The model we propose addresses this problem by representing the search for the images similar to a given image as the exploration of a lattice of (non-disjoint) image clusters, induced by a natural ordering criterion, based on similarity measures. The exploration proceeds in one of two basic ways: by querying, the user can jump to any cluster of the lattice, by specifying the criteria that the sought cluster must satisfy; by navigation: from any cluster, the user can move to a neighbor cluster, thus exploiting the ordering amongst clusters.
Subject Image retrieval
H.3.3 Information Search and Retrieval


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