An improved feature image matching algorithm based on Locality-Sensitive Hashing

Abstract

Image matching is a classic technique in computer vision. However, the traditional local invariant features image matching algorithm has two problems, narrow scale range and long time consuming. Aiming at these problems, we proposed a fast image matching algorithm with the aid of improved local invariant features based on Locality-Sensitive Hashing. Firstly, by building simple Gaussian pyramid and achieving FAST keypoint detection, keypoints are extracted from the reference image and the candidate matching image. Then Fast Retina Keypoint feature descriptor is calculated and weighted. Furthermore, the high-dimensional data is mapped to a low dimensional space and hash indexes are built through the local sensitive hashing algorithm in aiming of finding the approximate nearest neighbor. The experimental results in different datasets indicate that the improved algorithm achieves real-time processing in image matching, and has better robustness and shorter processing time than most classical methods.