Photo Aesthetics Ranking Network with Attributes and Content Adaptation
Abstract
Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics. However, previous methods for image aesthetics analysis have primarily focused on the coarse, binary categorization of images into high- or low-aesthetic categories. In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated photo aesthetics rating problem.
Cite
Text
Kong et al. "Photo Aesthetics Ranking Network with Attributes and Content Adaptation." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46448-0_40Markdown
[Kong et al. "Photo Aesthetics Ranking Network with Attributes and Content Adaptation." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/kong2016eccv-photo/) doi:10.1007/978-3-319-46448-0_40BibTeX
@inproceedings{kong2016eccv-photo,
title = {{Photo Aesthetics Ranking Network with Attributes and Content Adaptation}},
author = {Kong, Shu and Shen, Xiaohui and Lin, Zhe and Mech, Radomír and Fowlkes, Charless C.},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {662-679},
doi = {10.1007/978-3-319-46448-0_40},
url = {https://mlanthology.org/eccv/2016/kong2016eccv-photo/}
}