Image retargeting by combining fast seam carving with neighboring probability (FSc_Neip) and scaling

Lifang Wu, Lijuan Wang, Shuang Liu, Qingyang Zheng, Y. Jing, Chang Wen Chen, Bo Yan
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引用次数: 1

Abstract

No single retargeting approach performs well on all images and all target sizes; therefore, hybrid algorithms are often considered as promising alternatives. However, most hybrid schemes are time consuming. In this paper, we propose a fast hybrid framework in which the Fast Content-Aware Image Distance (FCAID) is used to connect fast seam carving with neighboring probability constraints (FSc_Neip) and scaling. FCAID is used to measure the image distance between the resized image given by FSc_Neip and the original image. This fast technique is embedded within the FSc_Neip framework. Our hybrid scheme is locally applied in strip regions. This makes the retargeting scheme globally non-homogeneous. Experimental results demonstrate that our approach comprehensively outperforms other state-of-the-art techniques in terms of image quality and computational complexity.
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结合快速缝雕刻与邻近概率(FSc_Neip)和缩放的图像重定位方法
没有一种单一的重定向方法对所有图像和所有目标尺寸都有效;因此,混合算法通常被认为是有前途的替代方案。然而,大多数混合方案都很耗时。本文提出了一种快速混合框架,利用快速内容感知图像距离(fast Content-Aware Image Distance, FCAID)将快速拼接与相邻概率约束(FSc_Neip)和缩放连接起来。FCAID用于测量由FSc_Neip给出的调整后的图像与原始图像之间的图像距离。这种快速技术嵌入在FSc_Neip框架中。该混合方案可局部应用于带状地区。这使得重定向方案在全局上不均匀。实验结果表明,我们的方法在图像质量和计算复杂度方面全面优于其他最先进的技术。
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