Heterogeneous image transformation

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Heterogeneous image transformation
	Wang, Nannan 1,2; Li, Jie 1; Tao, Dacheng2 ; Li, Xuelong3 ; Gao, Xinbo 1
刊名	pattern recognition letters
	2013
卷号	34 期号:1 页码:77-84
关键词	Heterogeneous image transformation Near infrared image Sketch-photo synthesis Sparse representation Support vector regression
英文摘要	heterogeneous image transformation (hit) plays an important role in both law enforcements and digital entertainment. some available popular transformation methods, like locally linear embedding based, usually generate images with lower definition and blurred details mainly due to two defects: (1) these approaches use a fixed number of nearest neighbors (nn) to model the transformation process, i.e., k-nn-based methods; (2) with overlapping areas averaged, the transformed image is approximately equivalent to be filtered by a low pass filter, which filters the high frequency or detail information. these drawbacks reduce the visual quality and the recognition rate across heterogeneous images. in order to overcome these two disadvantages, a two step framework is constructed based on sparse feature selection (sfs) and support vector regression (svr). in the proposed model, sfs selects nearest neighbors adaptively based on sparse representation to implement an initial transformation, and subsequently the svr model is applied to estimate the lost high frequency information or detail information. finally, by linear superimposing these two parts, the ultimate transformed image is obtained. extensive experiments on both sketch-photo database and near infrared-visible image database illustrates the effectiveness of the proposed heterogeneous image transformation method. (c) 2012 elsevier b.v. all rights reserved.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence
研究领域[WOS]	computer science
关键词[WOS]	face sketch synthesis ; large underdetermined systems ; recognition algorithms ; superresolution ; information ; equations
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000311927600010
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/23174]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Wang, Nannan,Li, Jie,Tao, Dacheng,et al. Heterogeneous image transformation[J]. pattern recognition letters,2013,34(1):77-84.
APA	Wang, Nannan,Li, Jie,Tao, Dacheng,Li, Xuelong,&Gao, Xinbo.(2013).Heterogeneous image transformation.pattern recognition letters,34(1),77-84.
MLA	Wang, Nannan,et al."Heterogeneous image transformation".pattern recognition letters 34.1(2013):77-84.