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).

本文提出了一种通过级联注意框回归和美感质量分类的深度学习建模来解决照片剪裁问题的方法。该神经网络使用由两个分支构成的结构，能够预测注意力包围框并分析美感特征，通过共享特征来提高计算效率。实验结果表明，该方法在照片剪裁方面取得了很高的效果，具有竞争性的结果和快速的处理速度（所有步骤运行速度为每秒 5 帧），即便是在有限的照片剪裁训练数据的情况下。