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This article describes the participation of the University and Hospitals of Geneva at the ImageCLEF 2006 image classification tasks (medical and non-medical). The techniques applied are based on classical tf/idf weightings of visual features as used in the GIFT (GNU Image Finding Tool). Based on the training data, features appearing in images of the same class are weighted higher than features appearing across classes. These feature weights are added to the classical weights. Several weightings and learning approaches are applied as well as quantisations of the features space with respect to grey levels. A surprisingly small number of grey levels leads to best results. Learning can improve the results only slightly and does not obtain as good results as classical image classification approaches. A combination of several classifiers leads to best final results, showing that the schemes have independent results.