Mining web data for image semantic annotation

Abstract

In this paper, an unsupervised image classification technique combining features from different media levels is proposed. In particular geometrical models of visual features are here integrated with textual descriptions derived through Information Extraction processes from Web pages. While the higher expressivity of the combined individual descriptions increases the complexity of the adopted clustering algorithms, methods for dimensionality reduction (i.e. LSA) are applied effectively. The evaluation on an image classification task confirms that the proposed Web mining model outperforms other methods acting on the individual levels for cost-effective annotation. © Springer-Verlag Berlin Heidelberg 2007.