| 一种基于数据聚类的鲁棒SIFT特征匹配方法 |
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| 引用本文： | 范志强,赵沁平.一种基于数据聚类的鲁棒SIFT特征匹配方法[J].计算机研究与发展,2012,49(5):1123-1129. |
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| 作者姓名： | 范志强 赵沁平 |
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| 作者单位： | 1. 虚拟现实技术与系统国家重点实验室(北京航空航天大学) 北京 100191
2. 北京航空航天大学计算机学院 北京 100191 |
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| 基金项目： | 国家"八六三"高技术研究发展计划基金项目 |
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| 摘 要： | 针对噪声敏感造成的SIFT特征匹配鲁棒性低问题,提出一种基于数据聚类的两阶段特征匹配方法.在满足特征匹配几何距离最邻近本质要求下扩展了k d数据结构,使其不但能够完成算术平均化匹配特征离线聚类,而且能够实现第1阶段聚类特征在线匹配.在此基础上,给出一种概率最优投票策略选择关键图像进行第2阶段匹配,最后合并两阶段属于关键图像的所有匹配特征对.实验结果表明,对于大量存在重叠关系的图像集合,该方法能够有效减少重复特征数量,降低噪声信息对特征匹配的干扰,极大地提高特征匹配的鲁棒性.
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| 关 键 词： | 聚类特征 鲁棒匹配 合成k-d树 SIFT 投票策略 |
A Data-Clustering Based Robust SIFT Feature Matching Method |
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| Fan Zhiqiang
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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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| Authors: | Fan Zhiqiang Zhao Qinping |
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| Affiliation: | 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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| 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. |
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| Keywords: | clustering feature robust matching composite k-d tree SIFT voting scheme |
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