LAUSR

The amount of visual data available on the Web is growing explosively and it is becoming increasingly important to explore methods for automatically estimating the quality of this content in a manner that is consistent with the aesthetic perceptions of humans. The key to this challenging problem is to design an appropriate set of features to extract the aesthetic properties from content. Most previous studies designed a set of aesthetic features based on photographic criteria, which were unavoidably limited to specific examples and they lacked an interpretation based on the mechanism of human aesthetic perception. According to psychological theory, visual complexity is an important property of the stimuli, because it directly influences the viewer’s arousal level, which is believed to be closely related to aesthetic perception. In this study, we propose an alternative set of features for aesthetic estimation based on a visual complexity principle. We extracted the visual complexity properties from an input image in terms of their composition, shape, and distribution. In addition, we demonstrated that the proposed features are consistent with human perception on the complexity in our visual complexity dataset. Next, we employed these features for photo-aesthetic quality estimation using a large-scale dataset. Various experiments were conducted under different conditions and comparisons with state-of-the-art methods shows that the proposed visual complexity feature outperforms photography rule-based features and even better than deep features.