  Since its beginning visual recognition research has tried to capture the huge
variability of the visual world in several image collections. The number of
available datasets is still progressively growing together with the amount of
samples per object category. However, this trend does not correspond directly
to an increasing in the generalization capabilities of the developed
recognition systems. Each collection tends to have its specific characteristics
and to cover just some aspects of the visual world: these biases often narrow
the effect of the methods defined and tested separately over each image set.
Our work makes a first step towards the analysis of the dataset bias problem on
a large scale. We organize twelve existing databases in a unique corpus and we
present the visual community with a useful feature repository for future
research.
