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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 452604, 12 pages
http://dx.doi.org/10.1155/2013/452604
Research Article

Robust Image Matching Algorithm Using SIFT on Multiple Layered Strategies

1School of Energy Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2School of Mechanical Engineering, The University of Adelaide, Adelaide 5005, Australia

Received 4 June 2013; Revised 18 October 2013; Accepted 1 November 2013

Academic Editor: Gradimir Milovanović

Copyright © 2013 Yong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As for the unsatisfactory accuracy caused by SIFT (scale-invariant feature transform) in complicated image matching, a novel matching method on multiple layered strategies is proposed in this paper. Firstly, the coarse data sets are filtered by Euclidean distance. Next, geometric feature consistency constraint is adopted to refine the corresponding feature points, discarding the points with uncoordinated slope values. Thirdly, scale and orientation clustering constraint method is proposed to precisely choose the matching points. The scale and orientation differences are employed as the elements of -means clustering in the method. Thus, two sets of feature points and the refined data set are obtained. Finally, 3 * delta rule of the refined data set is used to search all the remaining points. Our multiple layered strategies make full use of feature constraint rules to improve the matching accuracy of SIFT algorithm. The proposed matching method is compared to the traditional SIFT descriptor in various tests. The experimental results show that the proposed method outperforms the traditional SIFT algorithm with respect to correction ratio and repeatability.