An image retargeting method based on saliency map optimization and seam carving

Abstract

A good image retargeting method can retain the important information of an image while changing its size. Image retargeting has been widely used in multi-size device displays and software thumbnail images. The existing image retargeting methods have some defects when they are used to process important regions of a large area and linear elements in the image. In this paper, an improved Seam Carving method is developed through optimizing the saliency map determination and operation flow. The saliency map is determined by Canny edge detection with adaptive threshold, Hough transforms for detecting straight lines, Yolo neural network and flood fill for sensitive area detection, etc. With these methods, the expression of essential information in pictures is improved. In the algorithm running process, the SC algorithm based on average energy and the similar simulated annealing algorithm based on seam neighborhood penalty are used to improve the running process of Seam Carving. Finally, experimental results on the open-source data set RetargetMe indicate that the proposed method achieves better performance in comparison with Seam-carving (SC), Shift-map (SM), Scale-and-stretch (SNS), and Warping methods.