Prosemantic features for content-based image retrieval
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2011Citation
10.1007/978-3-642-18449-9_8
Adaptive Multimedia Retrieval. Understanding Media and Adapting to the User: 7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009, Revised Selected Papers. Lecture Notes in Computer Science, Volumen 6535. Springer, 2011. 87-100.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-642-18448-2 (print); 978-3-642-18449-9 (online)DOI
10.1007/978-3-642-18449-9_8Editor's Version
http://dx.doi.org/10.1007/978-3-642-18449-9_8Subjects
Information Systems Applications; Information Storage and Retrieval; Database Management; Multimedia Information Systems; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-18449-9_8Revised Selected Papers of 7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009
Rights
© Springer-Verlag Berlin Heidelberg 2011Abstract
We present here, an image description approach based on prosemantic features. The images are represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Prosemantic features are obtained by packing together the scores. To verify the effectiveness of the approach, we designed a target search experiment in which both low-level and prosemantic features are embedded into a content-based image retrieval system exploiting relevance feedback. The experiments show that the use of prosemantic features allows for a more successful and quick retrieval of the query images.
Files in this item
Google Scholar:Ciocca, Gianluigi
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Cusano, Claudio
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Santini, Simone
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Schettini, Raimondo
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