Several branches of computer vision heavily rely (but we could even say depend) on the availability of large datasets of labelled images. While such labeling is usually done by hand, a powerful help can be obtained from Internet and its related tools. In this paper we address the problem of automatically generating a set of images representing an object class, given the name of the class. We exploit semantic technologies, such as lexical resources and ontologies, in order to improve the search

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... erformances by using a standard web search engine. We will also discuss an application to the automatic building of a training set for a classification framework. Preliminary experiments are provided for 10 classes from the public CalTech256 dataset and results show an average increment in classification accuracy of about 10%.