光学精密工程2011，Vol.19Issue(6)：1375-1383,9.DOI:10.3788/OPE.20111906.1375
基于改进局部敏感散列算法的图像配准
Image registration based on extended LSH
摘要
Abstract
In order to realize quickly and accurately matching between the image features, an efficient high-dimensional feature vector retrieval algorithm, Extended Locality Sensitive Hashing(ELSH), was proposed based on LSH(Locality Sensitive Hashing). Firstly, the Scale Invariant Feature Transform (SIFT)algorithm was used to get the special point of an image and its features. Then, according to the sub-vectors selected randomly from the SIFT features, a hash index structure was built to reduce the indexing dimension and the searching scope. Thus, it can significantly reduce the time cost of indexing. Finally, the Random Sample Consensus (RANSAC)algorithm was used to select the right feature point pairs. Experimental results indicate that compared with the Best-Bin-First(BBF)and the LSH algorithm, ELSH algorithm not only ensures the accuracy of matching points, but also reduces the matching time. The time cost of ELSH only takes 50.1% of that of the BBF, and 62. 1% of that of the LSH. In conclusion, the proposed algorithm can quickly and precisely achieve the registration between images.关键词
尺度不变特征变换；特征匹配；局部敏感散列；改进的局部敏感散列Key words
Scale Invariant Feature Transform(SIFT)/feature matching/Locality Sensitive Hashing(LSH)/Extended LSH(ELSH)分类
信息技术与安全科学引用本文复制引用
龚卫国,张旋,李正浩..基于改进局部敏感散列算法的图像配准[J].光学精密工程,2011,19(6):1375-1383,9.基金项目
国家863高技术研究发展计划资助项目(No.2007AA01Z423)；公安部应用创新项目(No.2010YYCXCQSJ074)；重庆市科技攻关重点项目(No.CSTC2009AB0175)；重庆市自然科学基金资助项目(No.CSTC2010BB2230)；中央高校基本科研业务费资助项目(No.CDJXS10122218) （No.2007AA01Z423）
