text: Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, which inevitably restricts its generality. Deep learning techniques may help to address this issue, but the challenging problem is that we need to build a large-scale image retargeting dataset for the training of deep retargeting models. However, building such a dataset requires huge human efforts. In this paper, we propose a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image retargeting with a single deep model, without relying on any explicit user annotations. Our idea is built on the reverse mapping from the retargeted images to the given input images. If the retargeted image has serious distortion or excessive loss of important visual information, the reverse mapping is unlikely to restore the input image well. We constrain this forward-reverse consistency by introducing a cyclic perception coherence loss. In addition, we propose a simple yet effective image retargeting network (IRNet) to implement the image retargeting process. Our IRNet contains a spatial and channel attention layer, which is able to discriminate visually important regions of input images effectively, especially in cluttered images. Given arbitrary sizes of input images and desired aspect ratios, our Cycle-IR can produce visually pleasing target images directly. Extensive experiments on the standard RetargetMe dataset show the superiority of our Cycle-IR. In addition, our Cycle-IR outperforms the Multiop method and obtains the best result in the user study. Code is available at this https URL.
  @cite_31: 
  @cite_29: We present a novel approach for interactive content-aware image resizing. The resizing is performed on warping a triangular mesh over the image, which captures the image saliency information as well as the underlying image features. The warped triangular mesh and the horizontal and vertical scales of all triangles are simultaneously obtained by a quadratic optimization which can be achieved by solving a sparse linear system. Our approach can preserve the shapes of curved features in the resized images. The resizing operation can be performed in an interactive rate which makes the proposed approach practically useful for realtime image resizing. To guarantee a foldover free resizing result, we modify the optimization to a standard quadratic programming. A number of experimental results have shown that our approach has obtained pleasing results and outperforms the previous approaches.
  @cite_20: We propose the space of axis-aligned deformations as the meaningful space for content-aware image retargeting. Such deformations exclude local rotations, avoiding harmful visual distortions, and they are parameterized in 1D. We show that standard warping energies for image retargeting can be minimized in the space of axis-aligned deformations while guaranteeing that bijectivity constraints are satisfied, leading to high-quality, smooth and robust retargeting results. Thanks to the 1D parameterization, our method only requires solving a small quadratic program, which can be done within a few milliseconds on the CPU with no precomputation overhead. We demonstrate how the image size and the saliency map can be changed in real time with our approach, and present results on various input images, including the RetargetMe benchmark. We compare our results with six other algorithms in a user study to demonstrate that the space of axis-aligned deformations is suitable for the problem at hand. © 2012 Wiley Periodicals, Inc.
summary: Different from the seam carving method in a discrete way, Panozzo @math @cite_20 propose to continuously transform the input image into the target image. Specifically, they overlay a uniform grid on the input image and assign different scaling factors to each grid cell. The scaling factors of important regions are large, and conversely, small. As a result, those important regions are preserved as much as possible, while less important regions are shrinking obviously. Wang @math @cite_31 propose a scale-and-stretch warping method. They calculate optimal scaling factors for image regions based on the edge and saliency map. Jin @math @cite_29 cast image retargeting problem into a quadratic program based on a triangular mesh. All these conventional approaches need to calculate an importance map based on low-level visual cues or other human priors. The poor quality of importance map significantly degrades the performance of these methods, which seriously limits their generality.