为实现图像间的快速准确配准，在局部敏感散列(LSH)算法基础上，提出一种高效的高维特征向量检索算法—改进的LSH(ELSH)算法用以图像特征间的检索配对，从而实现图像间的配准。该配准算法首先采用尺度不变特征变换(SIFT)算法提取图像的特征点并进行描述，得到图像的高维特征向量。然后，根据随机选择的若干子向量构建哈希索引结构，以缩减构建索引数据的维数和搜索的范围，从而缩短建立索引的时间。最后，根据数据随机取样一致性(RANSAC)剔除错误点。实验结果表明，与BBF (Best- Bin- First)和LSH算法相比，ELSH算法不但提高了匹配点对的准确性同时也缩短了匹配时间，其特征匹配时间分别减少了49.9%和37.9%。实验表明该算法可以快速、精确地实现图像间的配准。

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.