Efficient image matching using weighted voting
► Establish correspondences by voting. ► Each candidate correspondence is treated not only as a candidate but also a voter. ► Optimal correspondences are computed by simple addition and ranking operations. ► More than one hundred times faster than the classical spectral method. Spectral decompositio...
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| Published in | Pattern recognition letters Vol. 33; no. 4; pp. 471 - 475 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-8655 1872-7344 1872-7344 |
| DOI | 10.1016/j.patrec.2011.02.008 |
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| Abstract | ► Establish correspondences by voting. ► Each candidate correspondence is treated not only as a candidate but also a voter. ► Optimal correspondences are computed by simple addition and ranking operations. ► More than one hundred times faster than the classical spectral method.
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. |
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| AbstractList | ► Establish correspondences by voting. ► Each candidate correspondence is treated not only as a candidate but also a voter. ► Optimal correspondences are computed by simple addition and ranking operations. ► More than one hundred times faster than the classical spectral method.
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. |
| Author | Shang, Mianyou Pang, Yanwei Yuan, Yuan Wang, Kongqiao |
| Author_xml | – sequence: 1 givenname: Yuan surname: Yuan fullname: Yuan, Yuan email: yuany@opt.ac.cn organization: Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, Shaanxi, China – sequence: 2 givenname: Yanwei surname: Pang fullname: Pang, Yanwei organization: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China – sequence: 3 givenname: Kongqiao surname: Wang fullname: Wang, Kongqiao organization: Nokia Research Center, Beijing 100176, China – sequence: 4 givenname: Mianyou surname: Shang fullname: Shang, Mianyou organization: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China |
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| Cites_doi | 10.1109/TPAMI.2004.1265866 10.1109/TCSVT.2008.924108 10.1561/0600000009 10.1016/0262-8856(92)90043-3 10.1016/S0031-3203(02)00054-7 10.1109/ICCV.2005.20 10.1109/TCSVT.2009.2020337 10.1016/j.sigpro.2010.08.010 10.1109/TKDE.2009.64 10.1109/ICCV.2009.5459319 10.1109/TPAMI.2006.134 10.5244/C.16.19 10.7551/mitpress/7503.003.0044 10.1023/B:VISI.0000029664.99615.94 10.1109/TKDE.2007.1003 10.1109/TPAMI.2008.70 |
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| Keywords | Spectral technique Correspondence establishment Weighted voting Image matching |
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| Title | Efficient image matching using weighted voting |
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