Learning an Aesthetic Photo Cropping Cascade

Abstract

Cropping is one of the most fundamental and common operations in image processing for improving the aesthetic quality of photographs. Instead of manually designing rules for cropping, in this paper, we propose a generative model that learns an aesthetic photo cropping cascade from a large database of well-composed images and a dataset containing images with crops generated by expert photographers. Specifically, this model includes cropping priori, intuitive likelihood, compositional likelihood and change likelihood. Our learning exploits a spatial pyramid saliency feature and a multi-level foreground segmentation. The inference is done by efficient sub window search (ESS) [10] which is benefited from the bound at conditional distribution in the cascade. Additionally, for extracting attentional subjects and capturing scene composition, we design an iterative saliency method to model the saliency moving paths, which is beyond the typical saliency model predicting a single attentional region. Experiments show that our approach outperforms the state-of-the-art cropping methods by a large margin.