This paper presents a new synthesis-based approach for batch image processing. Unlike existing tools that can only apply global edits to the entire image, our method can apply fine-grained edits to individual objects within the image. For example, our method can selectively blur or crop specific objects that have a certain property. To facilitate such fine-grained image editing tasks, we propose a neuro-symbolic domain-specific language (DSL) that combines pre-trained neural networks for image classification with other language constructs that enable symbolic reasoning. Our method can automatically learn programs in this DSL from user demonstrations by utilizing a novel synthesis algorithm. We have implemented the proposed technique in a tool called ImageEye and evaluated it on 50 image editing tasks. Our evaluation shows that ImageEye is able to automate 96% of these tasks.
@article{arxiv.2304.03253,
title = {ImageEye: Batch Image Processing Using Program Synthesis},
author = {Celeste Barnaby and Qiaochu Chen and Roopsha Samanta and Isil Dillig},
journal= {arXiv preprint arXiv:2304.03253},
year = {2023}
}