  We model the photo cropping problem as a cascade of attention box regression
and aesthetic quality classification, based on deep learning. A neural network
is designed that has two branches for predicting attention bounding box and
analyzing aesthetics, respectively. The predicted attention box is treated as
an initial crop window where a set of cropping candidates are generated around
it, without missing important information. Then, aesthetics assessment is
employed to select the final crop as the one with the best aesthetic quality.
With our network, cropping candidates share features within full-image
convolutional feature maps, thus avoiding repeated feature computation and
leading to higher computation efficiency. Via leveraging rich data for
attention prediction and aesthetics assessment, the proposed method produces
high-quality cropping results, even with the limited availability of training
data for photo cropping. The experimental results demonstrate the competitive
results and fast processing speed (5 fps with all steps).
