Deep Reinforcement Learning for Automatic Thumbnail Generation

Abstract

An automatic thumbnail generation method based on deep reinforcement learning (called RL-AT) is proposed in this paper. Differing from previous saliency-based and deep learning-based methods which predict the location and size of a rectangle region, our method models the thumbnail generation as predicting a rectangle region by cutting along four edges of the rectangle. We project the thumbnail cutting operations as a four step Markov decision-making process in the framework of deep Reinforcement learning. The best crop location in each cutting step is learned by using a deep Q-network. The deep Q-network gets observations from the recent image and selects an action from the action space. Then the deep Q-network receives feedback based on current selected action as reward. The action space and reward function are specifically designed for the thumbnail generation problem. A data set with more than 70,000 thumbnail annotations is used to train our RL-AT model. Our RL-AT model can efficiently generate thumbnails with low computational complexity, and 0.09 s is needed to generate a thumbnail image. Experiments have shown that our RL-AT model outperforms related methods in the thumbnail generation.