Ensemble Network For Ranking Images Based On Visual Appeal

We propose a computational framework for ranking images (group photos) taken at the same event within a short time span. The ranking is expected to correspond with human perception of overall appeal of the images. We hypothesize (and provide evidence through subjective analysis) that the factors that appeal to humans are its emotional content, aesthetics and image quality. We propose a network which is an ensemble of three information channels, each predicting a score corresponding to one of three visual appeal factors. For group emotion estimation, we propose a convolutional neural network (CNN) architecture for predicting group emotion from images. This architecture enforces the network to put emphasis on the important regions in the images, and achieves comparable results to the state-of-the-art. Next, we develop a network for the image ranking task that combines group emotion, aesthetics and image quality scores. Owing to the unavailability of suitable databases, we created a new database of manually annotated group photos taken during various social events. We present experimental results on this database and other benchmark databases whenever available. Overall, our experiments show that the proposed framework can reliably predict the overall appeal of a image with results closely corresponding to human ranking.