Key dimension filtering based search algorithm of B+Tree for image feature matching

In dealing with the issues of low efficiency and low accuracy in multiple wide-based-line image matching, this paper adopts the classical SIFT descriptor, and proposes a novel high dimensional feature search algorithm. This paper follows the distance-based similarity standard, and firstly partitions the image feature set into different classes, then establishes a B+Tree for each class, and finally gives out a key dimension filtering strategy(KDF) in the KNN search step to speed up the high dimensional feature matching. Experimental results show that the proposed algorithm, which can obtain a higher accuracy with a lower time cost than the classical KNN search algorithm such as BBF, LSH and so on, would be a help to improve the capability of multiple wide-based-line image matching.