Context-Aware Video Retargeting via Graph Model

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	Context-Aware Video Retargeting via Graph Model
	Qu, Zhan 1; Wang, Jinqiao1 ; Xu, Min 2; Lu, Hanqing1
刊名	IEEE TRANSACTIONS ON MULTIMEDIA
	2013-11-01
卷号	15 期号:7 页码:1677-1687
关键词	Context-aware grid graph model spatial-temporal correlation video retargeting
英文摘要	Video retargeting is a crowded but challenging research area. In order to maximally comfort the viewers' watching experience, the most challenging issue is how to retain the spatial shape of important objects while ensure temporal smoothness and coherence. Existing retargeting techniques deal with these spatial-temporal requirements individually, which preserve the spatial geometry and temporal coherence for each region. However, the spatial-temporal property of the video content should be context-relevant, i.e., the regions belonging to the same object are supposed to undergo uniform spatial-temporal transformation. Regardless of the contextual information, the divide-and-rule strategy of existing techniques usually incurs various spatial-temporal artifacts. In order to achieve satisfactory spatial-temporal coherent video retargeting, in this paper, a novel context-aware solution is proposed via graph model. First, we employ a grid-based warping framework to preserve the spatial structure and temporal motion trend at the unit of grid cell. Second, we propose a graph-based motion layer partition algorithm to estimate motions of different regions, which simultaneously provides the evaluation of contextual relationship between grid cells while estimating the motions of regions. Third, complementing the salience-based spatial-temporal information preservation, two novel context constraints are encoded for encouraging the grid cells of the same object to undergo uniform spatial and temporal transformation, respectively. Finally, we formulate the objective function as a quadratic programming problem. Our method achieves a satisfactory spatial-temporal coherence while maximally avoiding the influence of artifacts. In addition, the grid-cell-wise motion estimation could be calculated every few frames, which obviously improves the speed. Experimental results and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our approach.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
研究领域[WOS]	Computer Science ; Telecommunications
关键词[WOS]	ENERGY MINIMIZATION
收录类别	SCI
语种	英语
WOS记录号	WOS:000325811800018
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3348]
专题	自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Technol Sydney, Sch Comp & Commun, INEXT, Sydney, NSW 2007, Australia
推荐引用方式 GB/T 7714	Qu, Zhan,Wang, Jinqiao,Xu, Min,et al. Context-Aware Video Retargeting via Graph Model[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2013,15(7):1677-1687.
APA	Qu, Zhan,Wang, Jinqiao,Xu, Min,&Lu, Hanqing.(2013).Context-Aware Video Retargeting via Graph Model.IEEE TRANSACTIONS ON MULTIMEDIA,15(7),1677-1687.
MLA	Qu, Zhan,et al."Context-Aware Video Retargeting via Graph Model".IEEE TRANSACTIONS ON MULTIMEDIA 15.7(2013):1677-1687.