Deep Supervised Image Retargeting
Yijing Mei, Xiaojie Guo, Di Sun, Gang Pan, Jiawan Zhang · 2021
Recent learning-based image retargeting methods have achieved significant improvement. However, two main is-sues remain in this challenging task: (i) it is difficult to build ground truth datasets for supervised learning; (ii) most methods are based on a certain operator, not suitable for various images with different target sizes. In this paper, for the first time, we address these issues by providing a deep supervised image retargeting solution. We introduce a new dataset1of 6, 576 pairs generated by multiple operators using Image Re-targeting Quality Assessment (IRQA) algorithm. We then develop a mult-operator image retargeting model named MR-GAN, which learns the deformation process of retargeted images using multiple methods and conducts retargeting operations in feature space. Experimental results validate the effectiveness as well as its superiority against state-of-the-art alternatives of the proposed approach.