Aesthetic image cropping is a practical but challenging task which aims at
finding the best crops with the highest aesthetic quality in an image.
Recently, many deep learning methods have been proposed to address this
problem, but they did not reveal the intrinsic mechanism of aesthetic
evaluation. In this paper, we propose an interpretable image cropping model to
unveil the mystery. For each image, we use a fully convolutional network to
produce an aesthetic score map, which is shared among all candidate crops
during crop-level aesthetic evaluation. Then, we require the aesthetic score
map to be both composition-aware and saliency-aware. In particular, the same
region is assigned with different aesthetic scores based on its relative
positions in different crops. Moreover, a visually salient region is supposed
to have more sensitive aesthetic scores so that our network can learn to place
salient objects at more proper positions. Such an aesthetic score map can be
used to localize aesthetically important regions in an image, which sheds light
on the composition rules learned by our model. We show the competitive
performance of our model in the image cropping task on several benchmark
datasets, and also demonstrate its generality in real-world applications.

该研究提出了一种可解释的图像裁剪模型，使用深度学习方法生成美学评分地图，在裁剪级别的美学评价中考虑图像的组合和显著性，从而实现高质量的美学图像裁剪。