关键词: 尺度不变特征变换, 特征描述子, 马氏距离, 欧氏距离, 随机抽样一致性

Abstract: For the feature descriptors’ dimensions based on the SIFT algorithm are too high, resulting to low speed, low matching rate and other issues, a kind of hierarchical radial partition method is proposed to construct feature descriptor. The feature point neighborhood is divided into 8 regions, counting 8 directions’ gradient direction histogram in each region to get a 64 dimensions descriptor, which the dimensions of feature descriptors are reduced by 50%. At the same time, because the Mahalanobis distance considering the correlation between the feature descriptor vectors, using the two-direction matching method of Mahalanobis distance instead of Euclidean distance when matching, the RANSAC method is used to eliminate the mismatch points. The theoretical analysis and simulation results show that improved SIFT algorithm retains SIFT algorithm for fuzzy, compression, rotation and scaling invariance advantages, improves the matching speed, and the average rate of true-match increases of 10%~15%.

Key words: Scale-Invariant Feature Transform（SIFT）, feature descriptor, Mahalanobis distance, Euclidean distance, Random Sample Consensus（RANSAC）