Abstract
As the perception of beauty is subjective across individuals, evaluating the objective aesthetic value of an image is a challenging task in image retrieval system. Unlike current online photo sharing services that take the average rating as the aesthetic score, our system integrates various ratings from different users by jointly modeling images and users' expertise in a regression framework. In the front-end, users are asked to rate images selected by an active learning process. A multi-observer regression model is employed in the back-end to integrate these ratings for predicting the aesthetic value of images. Moreover, the system can be incorporated into current photo sharing services as complement by providing more accurate ratings. © 2013 Springer-Verlag.
Cite
CITATION STYLE
Xiao, H., Xiao, H., & Eckert, C. (2013). OPARS: Objective photo aesthetics ranking system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7814 LNCS, pp. 861–864). https://doi.org/10.1007/978-3-642-36973-5_103
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