Automatic image cropping with aesthetic map and gradient energy map

Yueying Kao, Ran He, Kaiqi Huang

Abstract: Image cropping is a fundamental task in image editing to enhance the aesthetic quality of images. In this paper, we propose an automatic image cropping technique based on aesthetic map and gradient energy map. Instead of utilizing aesthetic rules in previous methods, we learn the aesthetic map by a deep convolutional neural network with a large-scale dataset for aesthetic quality assessment. The aesthetic map can highlight the discriminative image regions for high (or low) aesthetic quality category. The gradient energy map presents edge spatial distribution of images and is developed to compute the simplicity of images. Then a composition model is learned with the aesthetic map and gradient energy map to evaluate the quality of composition for crops. Moreover, an aesthetic preservation model is developed to compute the aesthetic information remained in crops to avoid cropping out high aesthetic regions. Experiments show that our approach significantly outperforms state-of-the-art cropping methods.