A Data-Clustering Based Robust SIFT Feature Matching Method

Fan Zhiqiang and Zhao Qinping

Journal of Computer Research and Development > 2012 > 49(5): 1123-1129.

Fan Zhiqiang and Zhao Qinping. A Data-Clustering Based Robust SIFT Feature Matching Method[J]. Journal of Computer Research and Development, 2012, 49(5): 1123-1129.

Citation:

Fan Zhiqiang and Zhao Qinping. A Data-Clustering Based Robust SIFT Feature Matching Method[J]. Journal of Computer Research and Development, 2012, 49(5): 1123-1129.

Citation:

Fan Zhiqiang and Zhao Qinping. A Data-Clustering Based Robust SIFT Feature Matching Method[J]. Journal of Computer Research and Development, 2012, 49(5): 1123-1129.

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A Data-Clustering Based Robust SIFT Feature Matching Method

Fan Zhiqiang and Zhao Qinping

(State Key Laboratory of Virtual Reality Technology and Systems (Beihang University), Beijing 100191) (School of Computer Science and Engineering, Beihang University, Beijing 100191)

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Published Date: May 14, 2012

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Abstract

Abstract

We present a data clustering method for robust SIFT matching. Our matching process contains an offline module to cluster features from a group of reference images and an online module to match them to the live images in order to enhance matching robustness. The main contribution lies in constructing a composite k-d data structure which can be used not only to cluster features but also to implement features matching. Then an optimal keyframe selection method is proposed using our composite k-d tree, which can not only put the matching process forward but also give us a way to employ a cascading feature matching strategy to combine matching results of composite k-d tree and keyframe. Experimental results show that our method dramatically enhances matching robustness.
- clustering feature,
- robust matching,
- composite k-d tree,
- SIFT,
- voting scheme

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