Personalized image quality assessment with Social-Sensed aesthetic preference

Abstract

Personalization is emerging as a key research objective for image aesthetics assessment, and how to incorporate personal preferences into aesthetics models is a crucial issue to be solved. Prior studies usually require users to explicitly express their aesthetic preferences in certain ways, which are time-consuming and labor-intensive. In this paper, inspired by the observation that human cognition and behavior influence each other, we propose to sense user aesthetic preferences from their favoring behavior on social media platforms. In this manner, personalized image aesthetics assessment can be realized without adding any extra burden to users. Towards this goal, we gather user favoring behavior over professional social photos and consider both user personal preference and common aesthetic standard to deal with the unreliability of user favoring behavior. Besides, we follow the idea of collaborative filtering and optimize the pairwise ranking between images to alleviate the data sparsity problem. Finally, a deep neural network architecture is developed for personalized aesthetics modeling. A simulated evaluation is carried out on two benchmark aesthetics datasets, even though users’ true preferences cannot be directly observed. The results demonstrate the potential of our approach for personalized image aesthetics assessment.