  Humans comprehend a natural scene at a single glance; painters and other
visual artists, through their abstract representations, stressed this capacity
to the limit. The performance of computer vision solutions matched that of
humans in many problems of visual recognition. In this paper we address the
problem of recognizing the genre (subject) in digitized paintings using
Convolutional Neural Networks (CNN) as part of the more general dealing with
abstract and/or artistic representation of scenes. Initially we establish the
state of the art performance by training a CNN from scratch. In the next level
of evaluation, we identify aspects that hinder the CNNs' recognition, such as
artistic abstraction. Further, we test various domain adaptation methods that
could enhance the subject recognition capabilities of the CNNs. The evaluation
is performed on a database of 80,000 annotated digitized paintings, which is
tentatively extended with artistic photographs, either original or stylized, in
order to emulate artistic representations. Surprisingly, the most efficient
domain adaptation is not the neural style transfer. Finally, the paper provides
an experiment-based assessment of the abstraction level that CNNs are able to
achieve.
