Abstract
In image matching, the oriented FAST and rotated BRIEF algorithm (ORB) has fast matching speed and less space. However, local features extracted do not have scale invariance. Its matching accuracy cannot meet some application requirements. In this paper, we present a novel matching algorithm, LSH-based image matching (LBIM), which is on the basis of local sensitive hash (LSH) to solve the problems mentioned above. In this work, there are three steps in image matching. First, contrast limited adaptive histogram equalization (CLAHE) is adopted to promote image quality. Then, improved ORB algorithm is used to extract and describe key points to meet the requirements of scale invariance and rotation invariance. Finally, LSH algorithm is employed on the descriptor to obtain the bucket number of each cluster. After pre-matching of LSH, progressive sample consensus (PROSAC) algorithm is used for rematching of key points to further improve the accuracy. The experiment results demonstrate LBIM proposed is more robust and accurate in different scenarios.
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