Content-aware image resizing with seam selection based on Gradient Vector Flow
Content-aware image resizing is an effective technique that allows to take into account the visual content of images during the resizing process. The basic idea beyond these algorithms is the resizing of an image by considering vertical and/or horizontal paths of pixels (i.e., seams) which contain low salient information. In this paper we exploit the Gradient Vector Flow (GVF) of the image to establish the paths to be considered during the resizing. The relevance of each path is derived from a saliency map obtained by considering the magnitude of the GVF associated to the image under consideration. The proposed technique has been tested, both qualitatively and quantitatively, by considering a representative set of images labeled with corresponding salient objects (i.e., ground-truth maps). Experimental results demonstrate that our method preserves crucial salient regions better than other state-of-the-art algorithms.