Heterogeneous image transformation

doi:10.1016/j.patrec.2012.04.005

OPT OpenIR > 光谱成像技术研究室

	Heterogeneous image transformation
	Wang, Nannan 1,2; Li, Jie 1; Tao, Dacheng2 ; Li, Xuelong3 ; Gao, Xinbo 1
	2013
发表期刊	PATTERN RECOGNITION LETTERS
卷号	34 期号:1 页码:77-84
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.
文章类型	Article
关键词	Heterogeneous Image Transformation Near Infrared Image Sketch-photo Synthesis Sparse Representation Support Vector Regression
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.patrec.2012.04.005
收录类别	SCI ; EI
关键词[WOS]	FACE SKETCH SYNTHESIS ; LARGE UNDERDETERMINED SYSTEMS ; RECOGNITION ALGORITHMS ; SUPERRESOLUTION ; INFORMATION ; EQUATIONS
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000311927600010
引用统计	被引频次：49[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/23174
专题	光谱成像技术研究室
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.

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