An accelerated matching algorithm for SIFT-like features

Abstract

This paper defines the SIFT-like features by analogy and proposes a novel method to accelerate its matching process. The acceleration strategy is to compute a characteristic value for each key point descriptor and divide a key point set into different subsets making use of this value. The approximate nearest neighbor (ANN) search method is applied to improve the efficiency of matching. The performance and accuracy of the proposed algorithm have been tested on various data and compared with the normal ANN search. The experimental results show the new method is, on average, twice faster than ANN search when it is applied to SIFT features' matching.