Measuring and Predicting Visual Importance of Similar Objects

doi:10.1109/TVCG.2016.2515614

	Measuring and Predicting Visual Importance of Similar Objects
	Kong, Yan1 ; Dong, Weiming1 ; Mei, Xing1 ; Ma, Chongyang 2; Lee, Tong-Yee 3; Lyu, Siwei 4; Huang, Feiyue 5; Zhang, Xiaopeng1
发表期刊	IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
	2016-12-01
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
DOI	10.1109/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	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11080
专题	多模态人工智能系统全国重点实验室_多媒体计算
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
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 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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