Scale invariant image matching using triplewise constraint and weighted voting

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

	Scale invariant image matching using triplewise constraint and weighted voting
	Pang, Yanwei 2; Shang, Mianyou 2; Yuan, Yuan1 ; Pan, Jing 3
刊名	neurocomputing
	2012-04-15
卷号	83 页码:64-71
关键词	Image matching Spectral technique Correspondence establishment Weighted voting
ISSN号	0925-2312
产权排序	2
合作状况	国内
英文摘要	due to limited computational resource, image matching on mobile phone places great demand on efficiency and scale invariant. though spectral matching (sm) with pairwisely geometric constraints is widely used in matching, it is not efficient and scale invariant for applications in mobile phones. the main factor that limits its efficiency is that it requires to eign-decomposition of a large affinity matrix when the number of candidate correspondences is large. it lacks scale invariance because the pairwise constraints cannot hold when large scale variation occurs. in this paper, we attempt to tackle these problems. in the proposed method, each candidate correspondence is considered as a voter and a candidate as well. as a voter it gives voting scores to other candidates and also votes itself. based on the voting scores, the optimal correspondences are computed by simple addition operations and ranking operations, which results in high efficiency. to make the proposed method scale invariant, we propose a novel triple-wisely geometric constraint formed by three potential correspondences with one being the candidate and the other two being voters. the three correspondences constitute a pair of triangles. the similarity of the two triangles is the core of the triple-wisely constraint, which is robust to scale variation. the information of triple-wise constraints are encoded in a 3-dimensional matrix from which the optimal correspondence can be obtained by simple summation and ranking operations. experimental results on real-data show the effectiveness and efficiency of the proposed method. (c) 2012 elsevier b.v. all rights reserved.
学科主题	computer science ; artificial intelligence
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]	relevance feedback ; subspace
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000301613800008
公开日期	2012-09-03
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/20261]
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.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China 2.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China 3.Tianjin Univ Educ & Technol, Sch Elect Engn, Tianjin 300222, Peoples R China
推荐引用方式 GB/T 7714	Pang, Yanwei,Shang, Mianyou,Yuan, Yuan,et al. Scale invariant image matching using triplewise constraint and weighted voting[J]. neurocomputing,2012,83:64-71.
APA	Pang, Yanwei,Shang, Mianyou,Yuan, Yuan,&Pan, Jing.(2012).Scale invariant image matching using triplewise constraint and weighted voting.neurocomputing,83,64-71.
MLA	Pang, Yanwei,et al."Scale invariant image matching using triplewise constraint and weighted voting".neurocomputing 83(2012):64-71.