Measuring and Predicting Visual Importance of Similar Objects
Kong, Yan1; Dong, Weiming1; Mei, Xing1; Ma, Chongyang2; Lee, Tong-Yee3; Lyu, Siwei4; Huang, Feiyue5; Zhang, Xiaopeng1
2016-12-01
发表期刊IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷号22期号:12页码:2564-2578
文章类型Article
摘要Similar objects are ubiquitous and abundant in both natural and artificial scenes. Determining the visual importance of several similar objects in a complex photograph is a challenge for image understanding algorithms. This study aims to define the importance of similar objects in an image and to develop a method that can select the most important instances for an input image from multiple similar objects. This task is challenging because multiple objects must be compared without adequate semantic information. This challenge is addressed by building an image database and designing an interactive system to measure object importance from human observers. This ground truth is used to define a range of features related to the visual importance of similar objects. Then, these features are used in learning-to-rank and random forest to rank similar objects in an image. Importance predictions were validated on 5,922 objects. The most important objects can be identified automatically. The factors related to composition (e.g., size, location, and overlap) are particularly informative, although clarity and color contrast are also important. We demonstrate the usefulness of similar object importance on various applications, including image retargeting, image compression, image re-attentionizing, image admixture, and manipulation of blindness images.
关键词Similar Objects Visual Importance Listwise Ranking
WOS标题词Science & Technology ; Technology
DOI10.1109/TVCG.2016.2515614
关键词[WOS]REPEATED SCENE ELEMENTS ; IMAGE ; DATABASE ; VIDEO
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61271430 ; Supporting Program for Sci & Tech Research of China(2015BAH53F00) ; CASIA-Tencent BestImage joint research project ; Ministry of Science and Technology, Taiwan(MOST-103-2221-E-006-106-MY3 ; National Science Foundation(IIS-0953373 ; 61372184 ; MOST-104-2221-E-006-044-MY3) ; CCF-1319800) ; 61331018 ; 61471359)
WOS研究方向Computer Science
WOS类目Computer Science, Software Engineering
WOS记录号WOS:000387360500006
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/11080
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Dong, Weiming
作者单位1.Chinese Acad Sci, Inst Automat, NLPR LIAMA, Beijing, Peoples R China
2.Univ Southern Calif, Dept Comp Sci, Los Angeles, CA 90089 USA
3.Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan, Taiwan
4.SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA
5.Tencent, Social Network Platform Dept, Shanghai, Peoples R China
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GB/T 7714
Kong, Yan,Dong, Weiming,Mei, Xing,et al. Measuring and Predicting Visual Importance of Similar Objects[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2016,22(12):2564-2578.
APA Kong, Yan.,Dong, Weiming.,Mei, Xing.,Ma, Chongyang.,Lee, Tong-Yee.,...&Zhang, Xiaopeng.(2016).Measuring and Predicting Visual Importance of Similar Objects.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,22(12),2564-2578.
MLA Kong, Yan,et al."Measuring and Predicting Visual Importance of Similar Objects".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 22.12(2016):2564-2578.
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