Targeting Accurate Object Extraction From an Image: A Comprehensive Study of NaturalImage Matting

	Targeting Accurate Object Extraction From an Image: A Comprehensive Study of NaturalImage Matting
	Qingsong Zhu; Ling Shao; Xuelong Li; Lei Wang
刊名	IEEE transactions on neural networks and learning systems
	2015
英文摘要	With the development of digital multimedia technologies, image matting has gained increasing interests from both academic and industrial communities. The purpose of image matting is to precisely extract the foreground objects with arbitrary shapes from an image or a video frame for further editing. It is generally known that image matting is inherently an ill-posed problem because we need to output three images out of only one input image. In this paper, we provide a comprehensive survey of the existing image matting algorithms and evaluate their performance. In addition to the blue screenmatting, we systematically divide all existing natural image matting methods into four categories: 1) color sampling-based; 2) propagation-based; 3) combination of sampling-based and propagation-based; and 4) learning-based approaches. Sampling-based methods assume that the foreground and background colors of an unknown pixel can be explicitly estimated by examining nearby pixels. Propagation-based methods are instead based on the assumption that foreground and background colors are locally smooth. Learning-based methods treat the matting process as a supervised or semisupervised learning problem. Via the learning process, users can construct a linear or nonlinear model between the alpha mattes and the imagecolors using a training set to estimate the alpha matte of an unknown pixel without any assumption about the characteristics of the testing image. With three benchmark data sets, the various matting algorithms are evaluated and compared using several metrics to demonstrate the strengths and weaknesses of each method both quantitatively and qualitatively. Finally, we conclude this paper by outlining the research trends and suggesting anumber of promising directions for future development.
收录类别	SCI
原文出处	http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=12&SID=V1ODnYALL5nJuq7w4rp&page=1&doc=1
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/7096]
专题	深圳先进技术研究院_医工所
作者单位	IEEE transactions on neural networks and learning systems
推荐引用方式 GB/T 7714	Qingsong Zhu,Ling Shao,Xuelong Li,et al. Targeting Accurate Object Extraction From an Image: A Comprehensive Study of NaturalImage Matting[J]. IEEE transactions on neural networks and learning systems,2015.
APA	Qingsong Zhu,Ling Shao,Xuelong Li,&Lei Wang.(2015).Targeting Accurate Object Extraction From an Image: A Comprehensive Study of NaturalImage Matting.IEEE transactions on neural networks and learning systems.
MLA	Qingsong Zhu,et al."Targeting Accurate Object Extraction From an Image: A Comprehensive Study of NaturalImage Matting".IEEE transactions on neural networks and learning systems (2015).