Content-aware image resizing: An improved and shadow-preserving seam carving method

The performance of seam carving-based image resizing algorithms is strongly dependent on the quality of the importance map extracted from the image. To date, various approaches have been proposed to extract the importance map, however, none has considered to take the shadows within the image into account. In most cases, existing shadows in the images would imply important information and help in better and quick understanding of the content of the images. This fact motivates us to keep them during the image resizing as much as we can. Therefore, in this paper, a seam carving-based algorithm is presented where we introduce an efficient shadow extraction algorithm in the direction of our motivation. We also propose a saliency map for highlighting the salient objects in the images. In addition to these two maps, a gradient map is also extracted to portray the details of the background. These maps are then combined in order to produce the final importance map. Extensive experiments conducted on a large collection of images indicate that the proposed method, in terms of preserving the main content of the input image, the shadows within it, and the important structures of the image, is superior to state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.