Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategy since they have different natures. We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image targeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
retargeting methods have been realized through image warping or mapping by using several deformation and smoothness constraints @cite_30 @cite_50 @cite_0 @cite_19 @cite_23 @cite_27 @cite_24 . A finite element method has also been used to formulate image warping @cite_37 . Recent continuous retargeting methods focus on preserving local structures. @cite_45 minimize warping energy in the space of axis-aligned deformations to avoid harmful distortions. @cite_47 couple mesh deformations with similarity transforms for line features to preserve line structure properties. @cite_33 present a patch-based scheme with an extended significance measurement to preserve shapes of both visual salient objects and structural lines. These approaches perform well on shape preservation of salient objects but often over-squeeze or over-stretch the T-regions to distort all texels since T-regions are usually not salient in the whole image.
@cite_30 @cite_37 @cite_33 @cite_0 @cite_19 @cite_27 @cite_24 @cite_45 @cite_23 @cite_50 @cite_47
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Image retargeting is the process of adapting images to fit displays with various aspect ratios and sizes. Most studies on image retargeting focus on shape preservation, but they do not fully consider the preservation of structure lines, which are sensitive to human visual system. In this paper, a patch-based retargeting scheme with an extended significance measurement is introduced to preserve shapes of both visually salient objects and structure lines while minimizing visual distortions. In the proposed scheme, a similarity transformation constraint is used to force visually salient content to undergo as-rigid-as-possible deformation, while an optimization process is performed to smoothly propagate distortions. These processes enable our approach to yield pleasing content-aware warping and retargeting. Experimental results and a user study show that our results are better than those generated by state-of-the-art approaches. 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. 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. 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.