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Proceedings Paper

Novel image matching confidence fusion evaluation algorithm based on support vector machine
Author(s): Lamei Zou; Zhiguo Cao; Tianxu Zhang
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Paper Abstract

Confidence evaluation is an important technique in image matching process. This paper proposes a confidence level evaluation method for image matching result based on support vector machine (SVM). We divide the matching result into two different types: the correct result and the wrong result. So we translate the match result's confidence evaluation problem into the matching result's classification. This paper firstly provides a method of how to prepare the character parameters which can accurately reflect the matching performance. And then the SVM based on Gaussian kernel is used as a classifier to classify the match result and discriminate the match result's type. The experiments show that this method is effective. Compared with the Dempster-Shafer (D-S) evidence reasoning fusion method it has much higher accuracy.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881D (15 November 2007); doi: 10.1117/12.749436
Show Author Affiliations
Lamei Zou, Huazhong Univ. of Science and Technology (China)
Zhiguo Cao, Huazhong Univ. of Science and Technology (China)
Tianxu Zhang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

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