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We propose a computational framework for ranking images (group photos inparticular) taken at the same event within a short time span. The ranking isexpected to correspond with human perception of overall appeal of the images.We hypothesize and provide evidence through subjective analysis that thefactors that appeal to humans are its emotional content, aesthetics and imagequality. We propose a network which is an ensemble of three informationchannels, each predicting a score corresponding to one of the three visualappeal factors. For group emotion estimation, we propose a convolutional neuralnetwork (CNN) based architecture for predicting group emotion from images. Thisnew architecture enforces the network to put emphasis on the important regionsin 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 suitabledatabases, we created a new database of manually annotated group photos takenduring various social events. We present experimental results on this databaseand other benchmark databases whenever available. Overall, our experiments showthat the proposed framework can reliably predict the overall appeal of imageswith results closely corresponding to human ranking.

Ensemble Network for Ranking Images Based on Visual Appeal