Efficient image matching using weighted voting
文献类型：期刊论文
| 作者 | Yuan, Yuan1 ; Pang, Yanwei2; Wang, Kongqiao3; Shang, Mianyou2
|
| 刊名 | pattern recognition letters
 |
| 出版日期 | 2012-03-01 |
| 卷号 | 33期号:4页码:471-475 |
| 关键词 | Image matching Spectral technique Correspondence establishment Weighted voting |
| ISSN号 | 0167-8655 |
| 产权排序 | 1 |
| 合作状况 | 国际 |
| 中文摘要 | spectral decomposition subject to pairwise geometric constraints is one of the most successful image matching (correspondence establishment) methods which is widely used in image retrieval, recognition, registration, and stitching. when the number of candidate correspondences is large, the eigen-decomposition of the affinity matrix is time consuming and therefore is not suitable for real-time computer vision. to overcome the drawback, in this letter we propose to treat each candidate correspondence not only as a candidate but also as a voter. as a voter, it gives voting scores to other candidate correspondences. based on the voting scores, the optimal correspondences are computed by simple addition and ranking operations. experimental results on real-data demonstrate that the proposed method is more than one hundred times faster than the classical spectral method while does not decrease the matching accuracy. |
| 英文摘要 | spectral decomposition subject to pairwise geometric constraints is one of the most successful image matching (correspondence establishment) methods which is widely used in image retrieval, recognition, registration, and stitching. when the number of candidate correspondences is large, the eigen-decomposition of the affinity matrix is time consuming and therefore is not suitable for real-time computer vision. to overcome the drawback, in this letter we propose to treat each candidate correspondence not only as a candidate but also as a voter. as a voter, it gives voting scores to other candidate correspondences. based on the voting scores, the optimal correspondences are computed by simple addition and ranking operations. experimental results on real-data demonstrate that the proposed method is more than one hundred times faster than the classical spectral method while does not decrease the matching accuracy. (c) 2011 published by elsevier b.v. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | 物理科学和化学 |
| 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:000300868400012 |
| 公开日期 | 2011-09-30 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/10563]  |
| 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, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 2.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China 3.Nokia Res Ctr, Beijing 100176, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yuan, Yuan,Pang, Yanwei,Wang, Kongqiao,et al. Efficient image matching using weighted voting[J]. pattern recognition letters,2012,33(4):471-475. |
| APA | Yuan, Yuan,Pang, Yanwei,Wang, Kongqiao,&Shang, Mianyou.(2012).Efficient image matching using weighted voting.pattern recognition letters,33(4),471-475. |
| MLA | Yuan, Yuan,et al."Efficient image matching using weighted voting".pattern recognition letters 33.4(2012):471-475. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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