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Proceedings Paper

A hybrid features based image matching algorithm
Author(s): Zhenbiao Tu; Tao Lin; Xiao Sun; Hao Dou; Delie Ming
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Paper Abstract

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.

Paper Details

Date Published: 14 December 2015
PDF: 8 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130H (14 December 2015); doi: 10.1117/12.2205426
Show Author Affiliations
Zhenbiao Tu, Beijing Electro-Mechanical Engineering Institute (China)
Tao Lin, Beijing Electro-Mechanical Engineering Institute (China)
Xiao Sun, Huazhong Univ. of Science and Technology (China)
Hao Dou, Huazhong Univ. of Science and Technology (China)
Delie Ming, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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