This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.
The approaches @cite_0 @cite_11 @cite_45 @cite_2 @cite_34 @cite_26 continuously transform an input image into an image of a target size. In Liu and Gleicher @cite_0 , a non-linear image warping is used to emphasize important parts of an image. Wolf @cite_11 try to reduce distortion by shrinking less important pixels and preserving important regions. A scale-and-stretch warping method is proposed by Wang @cite_45 . Their method iteratively computes optimal scaling factors for local regions and updates a warped image assisted by edge and saliency map. Guo @cite_34 suggest a mesh representation based on image structures to preserve the shape of an input image. An interactive content-aware retargeting is proposed by Jin @cite_26 . They formulate retargeting using a sparse linear system based on a triangular mesh, and then solve it via quadratic optimization.
@cite_26 @cite_0 @cite_45 @cite_2 @cite_34 @cite_11
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We present a "scale-and-stretch" warping method that allows resizing images into arbitrary aspect ratios while preserving visually prominent features. The method operates by iteratively computing optimal local scaling factors for each local region and updating a warped image that matches these scaling factors as closely as possible. The amount of deformation of the image content is guided by a significance map that characterizes the visual attractiveness of each pixel; this significance map is computed automatically using a novel combination of gradient and salience-based measures. Our technique allows diverting the distortion due to resizing to image regions with homogeneous content, such that the impact on perceptually important features is minimized. Unlike previous approaches, our method distributes the distortion in all spatial directions, even when the resizing operation is only applied horizontally or vertically, thus fully utilizing the available homogeneous regions to absorb the distortion. We develop an efficient formulation for the nonlinear optimization involved in the warping function computation, allowing interactive image resizing. Image retargeting aims to adapt images to displays of small sizes and different aspect ratios. Effective retargeting requires emphasizing the important content while retaining surrounding context with minimal visual distortion. In this paper, we present such an effective image retargeting method using saliency-based mesh parametrization. Our method first constructs a mesh image representation that is consistent with the underlying image structures. Such a mesh representation enables easy preservation of image structures during retargeting since it captures underlying image structures. Based on this mesh representation, we formulate the problem of retargeting an image to a desired size as a constrained image mesh parametrization problem that aims at finding a homomorphous target mesh with desired size. Specifically, to emphasize salient objects and minimize visual distortion, we associate image saliency into the image mesh and regard image structure as constraints for mesh parametrization. Through a stretch-based mesh parametrization process we obtain the homomorphous target mesh, which is then used to render the target image by texture mapping. The effectiveness of our algorithm is demonstrated by experiments. 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. The aim of this paper is to achieve seamless image stitching without producing visual artifact caused by severe intensity discrepancy and structure misalignment, given that the input images are roughly aligned or globally registered. Our new approach is based on structure deformation and propagation for achieving the overall consistency in image structure and intensity. The new stitching algorithm, which has found applications in image compositing, image blending, and intensity correction, consists of the following main processes. Depending on the compatibility and distinctiveness of the 2D features detected in the image plane, single or double optimal partitions are computed subject to the constraints of intensity coherence and structure continuity. Afterwards, specific 1D features are detected along the computed optimal partitions from which a set of sparse deformation vectors is derived to encode 1D feature matching between the partitions. These sparse deformation cues are robustly propagated into the input images by solving the associated minimization problem in gradient domain, thus providing a uniform framework for the simultaneous alignment of image structure and intensity. We present results in general image compositing and blending in order to show the effectiveness of our method in producing seamless stitching results from complex input images.