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14 December 2015 A hybrid features based image matching algorithm
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Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130H (2015)
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
In this paper, we present a novel image matching method to find the correspondences between two sets of image interest points. The proposed method is based on a revised third-order tensor graph matching method, and introduces an energy function that takes four kinds of energy term into account. The third-order tensor method can hardly deal with the situation that the number of interest points is huge. To deal with this problem, we use a potential matching set and a vote mechanism to decompose the matching task into several sub-tasks. Moreover, the third-order tensor method sometimes could only find a local optimum solution. Thus we use a cluster method to divide the feature points into some groups and only sample feature triangles between different groups, which could make the algorithm to find the global optimum solution much easier. Experiments on different image databases could prove that our new method would obtain correct matching results with relatively high efficiency.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhenbiao Tu, Tao Lin, Xiao Sun, Hao Dou, and Delie Ming "A hybrid features based image matching algorithm", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130H (14 December 2015);


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