基于感知结构相似度的多算子重定位

Yuming Fang, Weisi Lin, Zhou Wang, Zhijun Fang, Long Xu, Yong Yang
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引用次数: 1

摘要

本文提出了一种新的多算子重定位算法,该算法采用三种调整算子,即裁剪、裁剪和裁剪。为了确定在每次迭代中应该使用哪个算子,我们采用结构相似度(SSIM)来评估原始图像和重定向图像之间的相似度,用于动态规划。由于原始图像和重目标图像的大小不同,因此使用SIFT流对原始图像和重目标图像进行密集对应,进行相似度评估。此外,视觉显著性被用来根据人类视觉系统(HVS)的特征对SSIM结果进行加权。在公共图像重定位数据库上的实验结果表明,该算法具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-operator retargeting based on perceptual structural similarity
We propose a new multi-operator retargeting algorithm by using three resizing operators of seam carving, scaling, and cropping iteratively. To determine which operator should be used at each iteration, we adopt structural similarity (SSIM) to evaluate the similarity between the original and retargeted images for the dynamic programming. Since the sizes of original and retargeted images are different, SIFT flow is used for dense correspondence between the original and retargeted images for similarity evaluation. Additionally, visual saliency is used to weight SSIM results based on the characteristics of the Human Visual System (HVS). Experimental results on a public image retargeting database show the promising performance of the proposed multi-operator retargeting algorithm.
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