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Perceptually Aware Image Retargeting for Mobile Devices
Zhou, Yinzuo1; Zhang, Luming2; Zhang, Chao3; Li, Ping4,5; Li, Xuelong6
Contribution Rank6

Retargeting aims at adapting an original high-resolution photograph/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile platforms (especially when retargeting videos). Typically, only low-level visual features are exploited, and human visual perception is not well encoded. In this paper, we propose a novel retargeting framework that rapidly shrinks a photograph/video by leveraging human gaze behavior. Specifically, we first derive a geometry-preserving graph ranking algorithm, which efficiently selects a few salient object patches to mimic the human gaze shifting path (GSP) when viewing a scene. Afterward, an aggregation-based CNN is developed to hierarchically learn the deep representation for each GSP. Based on this, a probabilistic model is developed to learn the priors of the training photographs that are marked as aesthetically pleasing by professional photographers. We utilize the learned priors to efficiently shrink the corresponding GSP of a retargeted photograph/video to maximize its similarity to those from the training photographs. Extensive experiments have demonstrated that: 1) our method requires less than 35 ms to retarget a 1024x768 photograph (or a 1280x720 video frame) on popular iOS/Android devices, which is orders of magnitude faster than the conventional retargeting algorithms; 2) the retargeted photographs/videos produced by our method significantly outperform those of its competitors based on a paired-comparison-based user study; and 3) the learned GSPs are highly indicative of human visual attention according to the human eye tracking experiments.


KeywordMobile Platform Retarget Perceptual Gaze Behavior Deep Feature Probabilistic Model
Indexed BySCI ; EI
WOS IDWOS:000426272000017
EI Accession Number20175004537170
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Hangzhou Normal Univ, Alibaba Res Ctr Complex Sci, Hangzhou 311121, Zhejiang, Peoples R China;
2.Zhejiang Univ, Coll Comp Sci, Hangzhou 310000, Zhejiang, Peoples R China;
3.Univ Illinois, Comp Sci Dept, Champaign, IL 61820 USA;
4.Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China;
5.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Jiangsu, Peoples R China;
6.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Zhou, Yinzuo,Zhang, Luming,Zhang, Chao,et al. Perceptually Aware Image Retargeting for Mobile Devices[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(5):2301-2313.
APA Zhou, Yinzuo,Zhang, Luming,Zhang, Chao,Li, Ping,&Li, Xuelong.(2018).Perceptually Aware Image Retargeting for Mobile Devices.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(5),2301-2313.
MLA Zhou, Yinzuo,et al."Perceptually Aware Image Retargeting for Mobile Devices".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.5(2018):2301-2313.
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