In this technical demonstration we present a content-based image retrieval system based on the 'query by example' paradigm. The system effectiveness will be proved for both category and target search on two standard image databases, even without a "good" initial example and ancillary information, such as device metadata, text annotations, etc. These results are obtained by incorporating in the system our recently proposed prosemantic features coupled with a relevance feedback mechanism, and by maximizing novelty and diversity in the result sets. © 2012 Springer-Verlag.
CITATION STYLE
Ciocca, G., Cusano, C., Santini, S., & Schettini, R. (2012). Prosemantic image retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7585 LNCS, pp. 643–646). Springer Verlag. https://doi.org/10.1007/978-3-642-33885-4_72
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