  Unsupervised learning from continuous visual streams is a challenging problem
that cannot be naturally and efficiently managed in the classic batch-mode
setting of computation. The information stream must be carefully processed
accordingly to an appropriate spatio-temporal distribution of the visual data,
while most approaches of learning commonly assume uniform probability density.
In this paper we focus on unsupervised learning for transferring visual
information in a truly online setting by using a computational model that is
inspired to the principle of least action in physics. The maximization of the
mutual information is carried out by a temporal process which yields online
estimation of the entropy terms. The model, which is based on second-order
differential equations, maximizes the information transfer from the input to a
discrete space of symbols related to the visual features of the input, whose
computation is supported by hidden neurons. In order to better structure the
input probability distribution, we use a human-like focus of attention model
that, coherently with the information maximization model, is also based on
second-order differential equations. We provide experimental results to support
the theory by showing that the spatio-temporal filtering induced by the focus
of attention allows the system to globally transfer more information from the
input stream over the focused areas and, in some contexts, over the whole
frames with respect to the unfiltered case that yields uniform probability
distributions.
