Efficient image matching using weighted voting

Authors:
Yuan Yuan;Yanwei Pang;Kongqiao Wang;Mianyou Shang
Affiliations:
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, ...;School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;Nokia Research Center, Beijing 100176, China;School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

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.