A novel binary local feature descriptor based on SIFT is proposed to avoid disadvantages such as high computational cost and large memory cost of SIFT

and low discriminative power and robustness from binary-valued descriptors such as BRIEF

ORB

BRISK

and FREAK. Traditional SIFT feature space and distribution of feature vectors are analyzed theoretically and experimentally. Based on the results

the SIFT algorithm is improved by combining the advantages of binary descriptors. Different from traditional binary descriptors

each component of the SIFT feature vector is sorted by magnitude

and median values are selected as quantization thresholds to transform the high-dimensional floating point SIFT feature vector to a bit vector. Similarity between key points is evaluated by the Hamming distance instead of the Euclidean distance to improve matching efficiency. Then

the binary descriptor is divided into two parts that are matched at the matching stage. The purpose is to eliminate invalid matching feature points to further reduce matching time. Extensive experiments on large databases demonstrate the strong discriminative power and robustness of our quantization methods. The binary feature descriptor proposed considers low memory cost and high matching efficiency while maintaining the strong discriminative power and robustness. The descriptor proposed solves the computational complexity from SIFT and the low discriminative power and robustness from binary descriptors. Moreover

an average of 77.5% invalid matching key points is eliminated to reduce the number of iterations of RANSAC. The proposed quantization algorithm can be used for fast image matching and fast image stitching to improve the efficiency of matching and stitching.