Overview of ProCrop's retrieval-based aesthetic cropping approach. (a) Construction of a professional image database and retrieval of images with similar compositional layouts. (b) Demonstration of ProCrop's cropping process, where a compositionally similar reference image guides the generation of aesthetically pleasing crop results.
The pipeline of ProCrop. Given an input image, ProCrop retrieves compositionally similar professional images and generates a textual description, which guide the model to produce aesthetically enhanced crops along with corresponding aesthetic scores.
Image cropping is crucial for enhancing the visual appeal and narrative impact of photographs, yet existing rule-based and data-driven approaches often lack diversity or require annotated training data. We introduce ProCrop, a retrieval-based method that leverages professional photography to guide cropping decisions. By fusing features from professional photographs with those of the query image, ProCrop learns from professional compositions, significantly boosting performance. Additionally, we present a large-scale dataset of 242K weakly-annotated images, generated by out-painting professional images and iteratively refining diverse crop proposals. This composition-aware dataset generation offers diverse high-quality crop proposals guided by aesthetic principles and becomes the largest publicly available dataset for image cropping. Extensive experiments show that ProCrop significantly outperforms existing methods in both supervised and weakly-supervised settings. Notably, when trained on the new dataset, our ProCrop surpasses previous weakly-supervised methods and even matches fully supervised approaches. Both the code and dataset will be made publicly available to advance research in image aesthetics and composition analysis.