Image retargeting aims to obtain high visual quality of target images for human vision. Through semantic segmentation and understanding of input images, we can better preserve the important semantic regions, so as to effectively improve the performance of image retargeting. Benefit from the successful application of deep neural network in the field of semantic segmentation, in this paper, we propose a novel image retargeting approach using semantic segmentation and pixel fusion. Compared with existing image retargeting methods, our approach can effectively reduce geometric distortion during image retargeting by finely reallocating scaling factors for each region based on the semantic segmentation results. Experimental results demonstrate that the proposed approach can well preserve important semantic regions while leaving less unnatural geometric distortion. Our approach also shows the important role of semantic segmentation and understanding of scenes in image retargeting in detail.

中文翻译：

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用于图像重定向的语义分割引导像素融合

图像重定位旨在为人类视觉获得目标图像的高视觉质量。通过对输入图像的语义分割和理解，我们可以更好地保留重要的语义区域，从而有效提高图像重定向的性能。受益于深度神经网络在语义分割领域的成功应用，在本文中，我们提出了一种使用语义分割和像素融合的新型图像重定向方法。与现有的图像重定向方法相比，我们的方法可以根据语义分割结果为每个区域精细地重新分配缩放因子，从而有效地减少图像重定向过程中的几何失真。实验结果表明，所提出的方法可以很好地保留重要的语义区域，同时减少不自然的几何失真。我们的方法还详细展示了语义分割和场景理解在图像重定向中的重要作用。