In this paper we applied the Caffe convolutional neural networ
k
(CNN) framework to portrait aesthetics quality
estimation. We
d
esign a set of semantic complexity features extracted from th
e
intermediate layer features of the CNN
architecture. Besides
,
CNN features trained for different task are compared in aesthe
t
ic
s estimation, and the contribution from
content is identifie
d
.
