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.
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Ciocca, G., Cusano, C., Santini, S., Schettini, R. (2011). Prosemantic Features for Content-Based Image Retrieval. In: Detyniecki, M., García-Serrano, A., Nürnberger, A. (eds) Adaptive Multimedia Retrieval. Understanding Media and Adapting to the User. AMR 2009. Lecture Notes in Computer Science, vol 6535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18449-9_8
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DOI: https://doi.org/10.1007/978-3-642-18449-9_8
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