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Green Image
Issue No. 05 - Sept.-Oct. (2013 vol. 33)
ISSN: 0272-1716
pp: 68-78
Yun Liang , South China Agric. Univ., China
Yong-Jin Liu , Tsinghua Univ., Beijing, China
Xiao-Nan Luo , SunYat-sen Univ., China
Lexing Xie , Australian Nat. Univ., Canberra, ACT, Australia
Xiaolan Fu , Chinese Acad. of Sci., Beijing, China
Image retargeting adjusts images to arbitrary sizes such that they can be viewed on different displays. Content-aware image retargeting has been receiving increased attention. In particular, researchers have improved a patch-wise scaling method for image retargeting at the object level. The scaling partitions the image into rectangular patches of adaptive sizes, which are comparable to the sizes of the salient objects in the image. This partitioning is based on a visual-saliency map; accordingly, the method labels the patches as important or unimportant. Then, the method scales the important patches as uniformly as possible and stretches or squeezes the unimportant patches to fit the target size. A patch-based image-similarity measure finds the optimal set of scaling factors. In experiments, the improved method performed well for three image types: lines and edges, foreground objects, and geometric structures.
Image edge detection, Object recognition, Electronic mail, Targeting, Human factors, Psychology

Yun Liang, Yong-Jin Liu, Xiao-Nan Luo, Lexing Xie and Xiaolan Fu, "Optimal-Scaling-Factor Assignment for Patch-wise Image Retargeting," in IEEE Computer Graphics and Applications, vol. 33, no. 5, pp. 68-78, 2013.
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