  Photographs are characterized by different compositional attributes like the
Rule of Thirds, depth of field, vanishing-lines etc. The presence or absence of
one or more of these attributes contributes to the overall artistic value of an
image. In this work, we analyze the ability of deep learning based methods to
learn such photographic style attributes. We observe that although a standard
CNN learns the texture and appearance based features reasonably well, its
understanding of global and geometric features is limited by two factors.
First, the data-augmentation strategies (cropping, warping, etc.) distort the
composition of a photograph and affect the performance. Secondly, the CNN
features, in principle, are translation-invariant and appearance-dependent. But
some geometric properties important for aesthetics, e.g. the Rule of Thirds
(RoT), are position-dependent and appearance-invariant. Therefore, we propose a
novel input representation which is geometry-sensitive, position-cognizant and
appearance-invariant. We further introduce a two-column CNN architecture that
performs better than the state-of-the-art (SoA) in photographic style
classification. From our results, we observe that the proposed network learns
both the geometric and appearance-based attributes better than the SoA.
