Synthetic photographs for learning aesthetic preferences
Photo ranking algorithms aim to quantify features within photographs to determine aesthetic quality and learn user preferences over these features. However, current benchmark corpora of photographs contain nonquantifiable contextual information. Moreover, they do not control the variance of quantifiable features. The recently released Panorama data set contains annotated synthetic images with controlled contextual features. The images lay along the full range of quantifiable features. This paper focuses on improving the performance of a predictive learning model trained on the pairwise preferences collected on these images. Predictive models were trained on individual as well as group preferences. Feature selection improved prediction accuracy from the 67% earlier reported to 91% accuracy on the group preference. On average, individual raters' preferences were predicted with 87% accuracy. Top features varied widely among individuals. However, the most frequent top three features were tilted horizon line, cropped objects, and number of objects. Copyright © 2013. Association for the Advancement of Artificial Intelligence. All rights reserved.
