Image Aesthetic Assessment Based on Pairwise Comparison a Unified Approach to Score Regression, Binary Classification, and Personalization
Abstract
We propose a unified approach to three tasks of aesthetic score regression, binary aesthetic classification, and personalized aesthetics. First, we develop a comparator to estimate the ratio of aesthetic scores for two images. Then, we construct a pairwise comparison matrix for multiple reference images and an input image, and predict the aesthetic score of the input via the eigenvalue decomposition of the matrix. By varying the reference images, the proposed algorithm can be used for binary aesthetic classification and personalized aesthetics, as well as generic score regression. Experimental results demonstrate that the proposed unified algorithm provides the state-of-the-art performances in all three tasks of image aesthetics.
Cite
Text
Lee and Kim. "Image Aesthetic Assessment Based on Pairwise Comparison a Unified Approach to Score Regression, Binary Classification, and Personalization." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00128Markdown
[Lee and Kim. "Image Aesthetic Assessment Based on Pairwise Comparison a Unified Approach to Score Regression, Binary Classification, and Personalization." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/lee2019iccv-image/) doi:10.1109/ICCV.2019.00128BibTeX
@inproceedings{lee2019iccv-image,
title = {{Image Aesthetic Assessment Based on Pairwise Comparison a Unified Approach to Score Regression, Binary Classification, and Personalization}},
author = {Lee, Jun-Tae and Kim, Chang-Su},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00128},
url = {https://mlanthology.org/iccv/2019/lee2019iccv-image/}
}