关键词: SIFT特征, 特征匹配, 相似性度量, 最近邻, 次近邻

Abstract:

In order to solve this problem, the authors reformed the form of similarity measurement of SIFT feature descriptors by using city-block distance instead of Euclidean distance to decrease the time complexity of the similarity measurement formula. Besides, a hypothesis algorithm about the nearest neighbor and the second- nearest neighbor was proposed,which supposed arbitrary two features in the image to be matched were the nearest neighbor point and the second- nearest neighbor point respectively and these two points can be replaced by comparing the distance of the current feature from other features in the image to be matched and the distance of the current feature from the supposed two features, finally the actual nearest neighbor point and the second- nearest neighbor point were gotten. The algorithm reduces the number of compares of features involved in the process of similarity computation and thereby decreases the amount of the computation of the algorithm. Experiments show that the proposed algorithm improves matching efficiency of SIFT features while keeping robustness unchanged, and which can provide safeguard for those applications with high real-time requirements.

Key words: SIFT(scale invariant feature transform) feature, feature matching, similarity measurement, nearest neighbor, second-nearest neighbor