We apply deep belief networks of restricted Boltzmann machines to bags of
words of sift features obtained from databases of 13 Scenes, 15 Scenes and
Caltech 256 and study experimentally their behavior and performance. We find
that the final performance in the supervised phase is reached much faster if
the system is pre-trained.