The availability of large annotated visual resources, such as ImageNet, recently led to important advances in image mining tasks. However, the manual annotation of such resources is cumbersome. Exploiting Web datasets as a substitute or complement is an interesting but challenging alternative. The main problems to solve are the choice of the initial dataset and the noisy character of Web text-image associations. This article presents an approach which first leverages Flickr groups to automatically build a comprehensive visual resource and then exploits it for image retrieval. Flickr groups are an interesting candidate dataset because they cover a wide range of user interests. To reduce initial noise, we introduce innovative and scalable image reranking methods. Then, we learn individual visual models for 38,500 groups using a low-level image representation. We exploit off-the-shelf linear models to ensure scalability of the learning and prediction steps. Finally,
image descriptions are obtained by concatenating prediction scores of individual models and by retaining only the most salient responses. To provide a comparison with a manually created resource, a similar pipeline is applied to ImageNet. Experimental validation is conducted on the ImageCLEF Wikipedia Retrieval 2010 benchmark, showing competitive results that demonstrate the validity of our approach.