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.
Jun-Tae Lee and Chang-Su Kim,
"Image Aesthetic Assessment Based on Pairwise Comparison - A Unified Approach to Score Regression, Binary Classification, and Personalization," accepted to International Conference on Computer Vision (ICCV), 2019.