This work presents the task of modifying images in an image editing program using natural language written commands. We utilize a corpus of over 6000 image edit text requests to alter real world images collected via crowdsourcing. A novel framework composed of actions and entities to map a user's natural language request to executable commands in an image editing program is described. We resolve previously labeled annotator disagreement through a voting process and complete annotation of the corpus. We experimented with different machine learning models and found that the LSTM, the SVM, and the bidirectional LSTM-CRF joint models are the best to detect image editing actions and associated entities in a given utterance.
@article{arxiv.1812.01083,
title = {A System for Automated Image Editing from Natural Language Commands},
author = {Jacqueline Brixey and Ramesh Manuvinakurike and Nham Le and Tuan Lai and Walter Chang and Trung Bui},
journal= {arXiv preprint arXiv:1812.01083},
year = {2018}
}