  As one of the fundamental techniques for image editing, image cropping
discards unrelevant contents and remains the pleasing portions of the image to
enhance the overall composition and achieve better visual/aesthetic perception.
In this paper, we primarily focus on improving the accuracy of automatic image
cropping, and on further exploring its potential in public datasets with high
efficiency. From this respect, we propose a deep learning based framework to
learn the objects composition from photos with high aesthetic qualities, where
an anchor region is detected through a convolutional neural network (CNN) with
the Gaussian kernel to maintain the interested objects' integrity. This initial
detected anchor area is then fed into a light weighted regression network to
obtain the final cropping result. Unlike the conventional methods that multiple
candidates are proposed and evaluated iteratively, only a single anchor region
is produced in our model, which is mapped to the final output directly. Thus,
low computational resources are required for the proposed approach. The
experimental results on the public datasets show that both cropping accuracy
and efficiency achieve the state-ofthe-art performances.
