DocumentCode :
1797083
Title :
Multi-operator retargeting based on perceptual structural similarity
Author :
Yuming Fang ; Weisi Lin ; Zhou Wang ; Zhijun Fang ; Long Xu ; Yong Yang
Author_Institution :
Sch. of Inf. Technol., Jiangxi Univ. of Finance & Econ., Nanchang, China
fYear :
2014
fDate :
9-13 July 2014
Firstpage :
416
Lastpage :
420
Abstract :
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.
Keywords :
dynamic programming; image processing; iterative methods; HVS characteristics; SIFT flow; SSIM; cropping; dense correspondence; dynamic programming; human visual system; iterative method; multioperator retargeting algorithm; original images; perceptual structural similarity; public image retargeting database; resizing operator; scale-invariant feature transform; scaling; seam carving; similarity evaluation; visual saliency; Algorithm design and analysis; Computer vision; Conferences; Educational institutions; Indexes; Visualization; Image Retargeting; Image Saliency; Multi-operator Retargeting; Structural Similarity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit & International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4799-5401-8
Type :
conf
DOI :
10.1109/ChinaSIP.2014.6889276
Filename :
6889276
Link To Document :
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