Knowledge Commons of Institute of Automation，CAS
| Image Retargetability | |
Tang, Fan1,2 ; Dong, Weiming1; Meng, Yiping3; Ma, Chongyang4; Wu, Fuzhang5; Li, Xinrui6; Lee, Tong-Yee7
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| Source Publication | IEEE TRANSACTIONS ON MULTIMEDIA
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| ISSN | 1520-9210 |
| 2020-03-01 | |
| Volume | 22Issue:3Pages:641-654 |
| Corresponding Author | Dong, Weiming(weiming.dong@ia.ac.cn) |
| Abstract | Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions while preserving its visually and semantically important content. However, not all images can be equally processed. This study introduces the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability, in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates the joint learning of meaningful photographic attributes and image content information, which can facilitate the regularization of the complicated retargetability rating problem. To train and analyze this model, we collect a dataset that contains retargetability scores and meaningful image attributes assigned by six expert raters. The experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show the applications of image retargetability in retargeting method selection, retargeting method assessment and generating a photo collage. |
| Keyword | Visualization Task analysis Distortion Measurement Image resolution Convolutional neural networks Semantics Image retargetability visual attributes multi-task learning deep convolutional neural network |
| DOI | 10.1109/TMM.2019.2932620 |
| WOS Keyword | OBJECTIVE QUALITY ASSESSMENT ; MODEL |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key R&D Program of China[2018YFC0807500] ; National Natural Science Foundation of China[61832016] ; National Natural Science Foundation of China[61672520] ; National Natural Science Foundation of China[61702488] ; Ministry of Science and Technology, Taiwan[108-2221-E-006-038-MY3] ; CASIA-Tencent Youtu joint research project |
| Funding Organization | National Key R&D Program of China ; National Natural Science Foundation of China ; Ministry of Science and Technology, Taiwan ; CASIA-Tencent Youtu joint research project |
| WOS Research Area | Computer Science ; Telecommunications |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| WOS ID | WOS:000519576700006 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/38614 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Dong, Weiming |
| Affiliation | 1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100864, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Didi Chuxing, Beijing 100094, Peoples R China 4.Kuaishou Technol, Beijing 100085, Peoples R China 5.Chinese Acad Sci, Inst Software, Beijing 100864, Peoples R China 6.North China Elect Power Univ, Dept Math & Phys, Beijing 102206, Peoples R China 7.Natl Cheng Kung Univ, Tainan 701, Taiwan |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Tang, Fan,Dong, Weiming,Meng, Yiping,et al. Image Retargetability[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2020,22(3):641-654. |
| APA | Tang, Fan.,Dong, Weiming.,Meng, Yiping.,Ma, Chongyang.,Wu, Fuzhang.,...&Lee, Tong-Yee.(2020).Image Retargetability.IEEE TRANSACTIONS ON MULTIMEDIA,22(3),641-654. |
| MLA | Tang, Fan,et al."Image Retargetability".IEEE TRANSACTIONS ON MULTIMEDIA 22.3(2020):641-654. |
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