Predicting Subjectivity in Image Aesthetics Assessment

International audience; Conventional image aesthetic quality prediction aims at predicting the average score of a picture or its aesthetic class (good/bad quality). However, aesthetic prediction is intrinsically subjective, and images with similar mean aesthetic scores/class might display very different levels of consensus by human raters. Recent work has dealt with aesthetic subjectiv-ity by predicting the distribution of human scores. However, predicting the distribution is not directly interpretable in terms of subjectivity, and might be sub-optimal compared to directly estimating subjectivity descriptors computed from ground-truth scores. In this paper, we propose several measures of subjectivity, ranging from simple statistical measures such as the standard deviation of the scores, to newly proposed descriptors inspired by information theory. We evaluate the prediction performance of these measures when they are computed from predicted score distributions or when they are directly learned from ground-truth data. We find that the latter strategy provides in general better results, though there is still a large space for improvement in aesthetic subjectivity prediction.