SIFT feature matching algorithm with global information
Yanjie Wang · Optics and Precision Engineering · 2009
An improved Scale Invariable Feature Transformation(SIFT) matching algorithm with global context vector is presented to solve the problems that SIFT descriptors result in a lot mismatches when an image has many similar regions.By detecting feature points in scale space,two kinds of feature vectors, a SIFT descriptor representing local properties and a global context vector,are computed.Then,according to BBF searching strategy,the feature vectors are matched by using Euclidean distance.The experimental results indicate that the improved algorithm can describe feature points in a larger region,and can reduce mismatch probability of experimental images from 19% to 11% because global context vectors based on global shape information are induced to the SIFT vectors based local Information.These results reported above show proposed algorithm improves matching results greatly.