Personalized photograph ranking and selection system considering positive and negative user feedback

Abstract

In this article, we propose a novel personalized ranking system for amateur photographs. The proposed framework treats the photograph assessment as a ranking problem and we introduce the idea of personalized ranking, which ranks photographs considering both their aesthetic qualities and personal preferences. Photographs are described using three types of features: photo composition, color and intensity distribution, and personalized features. An aesthetic prediction model is learned from labeled photographs by using the proposed image features and RBF-ListNet learning algorithm. The experimental results show that the proposed framework outperforms in the ranking performance: a Kendall's tau value of 0.432 is significantly higher than those obtained by the features proposed in one of the state-of-the-art approaches (0.365) and by learning based on support vector regression (0.384). To realize personalization in ranking, three approaches are proposed: the feature-based approach allows users to select photographs with specific rules, the example-based approach takes the positive feedback from users to rerank the photograph, and the list-based approach takes both positive and negative feedback from users into consideration. User studies indicate that all three approaches are effective in both aesthetic and personalized ranking.