Abstract:

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.

Key words: high-dimensional vector;feature matching;iSVD;the key dimension;nearest-neighbor searching