User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2106.07488v1
• Date: Mon, 14 Jun 2021 15:19:48 GMT
• Title: User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning
• Authors: Pei Lv, Jianqi Fan, Xixi Nie, Weiming Dong, Xiaoheng Jiang, Bing Zhou, Mingliang Xu and Changsheng Xu
• Abstract summary: We propose a novel user-guided personalized image aesthetic assessment framework. It leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL) It generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.
• Score: 64.07820203919283
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Personalized image aesthetic assessment (PIAA) has recently become a hot topic due to its usefulness in a wide variety of applications such as photography, film and television, e-commerce, fashion design and so on. This task is more seriously affected by subjective factors and samples provided by users. In order to acquire precise personalized aesthetic distribution by small amount of samples, we propose a novel user-guided personalized image aesthetic assessment framework. This framework leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users. It mainly consists of two stages. In the first stage, personalized aesthetic ranking is generated by interactive image enhancement and manual ranking, meanwhile two policy networks will be trained. The images will be pushed to the user for manual retouching and simultaneously to the enhancement policy network. The enhancement network utilizes the manual retouching results as the optimization goals of DRL. After that, the ranking process performs the similar operations like the retouching mentioned before. These two networks will be trained iteratively and alternatively to help to complete the final personalized aesthetic assessment automatically. In the second stage, these modified images are labeled with aesthetic attributes by one style-specific classifier, and then the personalized aesthetic distribution is generated based on the multiple aesthetic attributes of these images, which conforms to the aesthetic preference of users better.

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