Emotion Attention-Aware Collaborative Deep Reinforcement Learning for Image Cropping
Xiaoyan Zhang, Zhuopeng Li, Jianmin Jiang
Abstract
This paper proposes a collaborative deep reinforcement learning model for automatic image cropping (called CDRL-IC). By modeling image cropping as a decision-making process of reinforcement learning, our model could generate optimal cropping result in a few moving and zooming steps. An image with good composition is a comprehensive result by considering the relative importance of objects and also the spatial organization of visual elements. Therefore, emotion attention information which indicates the relationship and importance between objects is applied together with contextual information of color image for image cropping. In order to sufficiently use the emotion attention map and the color image, they are processed by two collaborative agents. The two agents make their primary learning separately and then share information through an information interaction module for making joint action prediction. In order to efficiently evaluate the cropping quality in the reward function, weighted Intersection Over Union (WIoU) is designed by integrating emotion attention map in the traditional IoU. Our CDRL-IC model is tested on a variety of datasets for both image cropping and thumbnail generation. The experiments show that our CDRL-IC model outperforms state-of-the-art methods on these benchmark datasets.