Deep Conditional Color Harmony Model for Image Aesthetic Assessment

As one of the important features, color provides plenty useful information to represent images. Thus, color harmony, which is defined as “two or more colors are sensed together as a single, pleasing, collective impression” [1], can also be served as a fundamental feature and plays a key role to determine the aesthetics quality of images. To reveal the inherent color harmony attribute within patch and the harmonious relations between image patches which construct the pleasing colorful images, we designed a conditional random field (CRF) based color harmony model in this paper to accomplish the image aesthetic assessment tasks. Unlike the previous learning based color harmony models, we used deep neural networks to obtain the coherence properties between original image patch pairs, and embedded these relations along with each patch's own color harmony characteristic into a CRF to measure the harmony scores of the entire image. The experimental results on a public dataset show that the proposed deep conditional color harmony model is superior to the existing color harmony models in respect of the image aesthetic assessment.