Seamless Image Editing for Perceptual Size Restoration Based on Seam Carving

Naohiko Ishikawa, Zhenyang Zhu, Jong-Nam Kim, Wan-Young Chung, Kentaro Go, Xiaoyang Mao

Abstract: Thing of interest (ToI) in a photograph may be perceived as smaller than being perceived from the real scene due to the discrepancy between the imaging principles in the camera and human perception. When using existing image resizing approaches to enlarge the ToI in the input image, the resulting image may have problems, such as loss of distance sense, composition collapse, failure to preserve salient object shapes, etc. In this study, we propose a ToI resizing method based on seam carving method. The proposed method adopts an energy function, which takes image composition preservation into consideration. Furthermore, to prevent salient objects from being edited, the state-of-the-art deep learning model for salient object detection (SOD) has been adopted in the proposed method. To confirm the performance of the proposed method, a subjective evaluation experiment was conducted in this study. The experimental result shows that the effectiveness of the proposed method in terms of the preservation of perceptual size and perceptual distance of the ToI.