Synthesized computational aesthetic evaluation of photos

Assessing aesthetic appeal of images is a highly subjective task which has attracted a lot of interests recently. It is an interdisciplinary subject related to art, psychology, and computer vision. In this paper, we systematically study prior researches of feature extraction in this area, and category them into four groups, low level, rule based, information theory, and visual attention. In each group, the effectiveness and limitations of existing features are examined. Based on the analysis, we propose a comprehensive feature set, which include 16 novel features and 70 well proved features. With this feature set, we build the system under machine learning scheme consisting of an SVM based classifier to estimate if an image is high aesthetic or low aesthetic. The experiments are conducted on public datasets show that our comprehensive feature set outperforms conventional models that concentrate mainly on certain types of features. The combination of our features produces a promising classification accuracy of 82.4% and a good performance comparable to aesthetic rating of human. Finally, we implemented the proposed evaluation system on mobile devices. It can provide real-time feedback to help users capture appealing photos.