  Image aesthetic evaluation has attracted much attention in recent years.
Image aesthetic evaluation methods heavily depend on the effective aesthetic
feature. Traditional meth-ods always extract hand-crafted features. However,
these hand-crafted features are always designed to adapt particu-lar datasets,
and extraction of them needs special design. Rather than extracting
hand-crafted features, an automati-cally learn of aesthetic features based on
deep convolutional neural network (DCNN) is first adopt in this paper. As we
all know, when the training dataset is given, the DCNN architecture with high
complexity may meet the over-fitting problem. On the other side, the DCNN
architecture with low complexity would not efficiently extract effective
features. For these reasons, we further propose a paralleled convolutional
neural network (PDCNN) with multi-level structures to automatically adapt to
the training dataset. Experimental results show that our proposed PDCNN
architecture achieves better performance than other traditional methods.
