基于子向量距离索引的特征匹配算法的改进

在解决高维向量的搜索问题方法中,基于子向量距离索引的向量匹配算法iSVD拥有较好的搜索精度和效率。但是,该算法计算复杂度仍然较高,在实际应用中会受到限制。针对该问题,引入关键维选取方法,对iSVD算法进行改进。该方法首先将特征向量划分为多个子向量;再通过某种筛选方法,选出部分子向量代替原特征向量,进而创建索引值;最后利用索引值进行最近邻搜索。该方法能够将相似性较小的特征向量进行有效的区分,且可以进一步缩小最近邻搜索的搜索范围。实验结果表明,该算法能够在保持良好搜索精度的同时,提高匹配的正确率,缩短匹配时间,具有较好的实用性。

Improved feature matching algorithm based on indexing sub-vector distance

The indexing Sub-Vector Distance ( iSVD ) based feature matching algorithm has good search accuracy and efficiency in feature matching of high dimensional vector,but the computational complexity of this algorithm is still high and it is limited in practical applications.Aiming at this problem,we introduce a key dimension selection method to improve the iSVD based feature matching algorithm.This method first divides the feature vector into several sub vectors,and selects some sub vectors to replace the original ones;then we create the index value for the feature vector,and search the nearest neighbor point according to the index value.This method can effectively differentiate the vectors with smaller similarity,and it can further reduce the search scope of the nearest neighbor.Experimental results show that the improved iSVD based feature matching algorithm has good search accuracy,and meanwhile improves the matching accuracy and shortens the time of matching,thus having a good practicability.