@inproceedings{77bc7921fcca4f0e895a8c3b0cec02e0,
title = "Image aesthetics assessment using Deep Chatterjee's machine",
abstract = "Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the Chatterjee's visual neuroscience model, we design Deep Chatterjee's Machine (DCM) tailored for this task. DCM first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend DCM to predicting the distribution of human ratings, since aesthetics ratings are often subjective. We also highlight our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to an effective data augmentation approach. Experimental results on the AVA dataset show that DCM gains significant performance improvement, compared to other state-of-the-art models.",
author = "Zhangyang Wang and Ding Liu and Shiyu Chang and Florin Dolcos and Beck, {Diane M} and Huang, {Thomas S}",
year = "2017",
month = jun,
day = "30",
doi = "10.1109/IJCNN.2017.7965953",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "941--948",
booktitle = "2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings",
address = "United States",
note = "2017 International Joint Conference on Neural Networks, IJCNN 2017 ; Conference date: 14-05-2017 Through 19-05-2017",
}