Prosemantic features for content-based image retrieval

Authors:
Gianluigi Ciocca;Claudio Cusano;Simone Santini;Raimondo Schettini
Affiliations:
Università degli Studi di Milano-Bicocca, Dipartimento di Informatica Sistemistica e Comunicazione, Milano, Italy;Università degli Studi di Milano-Bicocca, Dipartimento di Informatica Sistemistica e Comunicazione, Milano, Italy;Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain;Università degli Studi di Milano-Bicocca, Dipartimento di Informatica Sistemistica e Comunicazione, Milano, Italy
Venue:
AMR'09 Proceedings of the 7th international conference on Adaptive multimedia retrieval: understanding media and adapting to the user
Year:
2009

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Abstract

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.