Unsupervised Learning of Saliency Concepts for Natural Image Classification and Retrieval

Authors:
A. Perina;M. Cristani;V. Murino
Affiliations:
Dipartimento di Informatica, Università degli studi di Verona, Verona, Italia 37134;Dipartimento di Informatica, Università degli studi di Verona, Verona, Italia 37134;Dipartimento di Informatica, Università degli studi di Verona, Verona, Italia 37134
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

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Abstract

In this paper, a novel multi-scale, statistical approach for natural image representation is presented. The approach selects, at different scales, sets of features that represent exclusively the most typical visual elements of several natural scene categories, disregarding other non-characteristic, clutter, elements. Such features provide also a robust image visual signature, useful for scene understanding, image classification and retrieval. The approach lies upon a structured generative model efficiently trained through variational learning. Results regarding image classification and retrieval prove the goodness of the approach.