1. A method, comprising: computing, by at least one computing device, statistics describing a first set of training images, the statistics including at least one statistic indicative of an image-composition energy that measures a composition of the training images; generating, by the at least one computing device, first digital content comprising a first imaging model based on the statistics; generating, by the at least one computing device, second digital content comprising a second imaging model configured as a machine learning model trained, at least in part, by comparing salient image regions of a second set of training images to the salient image regions included in croppings of the second set of training images; generating, by the at least one computing device, third digital content comprising a learned composition model by combining at least the first and second imaging models into an ensemble of models; determining, by the at least one computing device, one or more cropped images from an input image based on the learned composition model by applying the ensemble of models to the input image in an order, the applying effective to reduce, after application of each model, a number of composition regions that are candidates from the input image for the one or more cropped images; and displaying, by the at least one computing device, cropped image representations of the one or more cropped images on a display device, the cropped image representations being user selectable to initiate cropping the input image and generating a cropped image that is cropped according to a selected one of the cropped image representations.