Using binarization and hashing for efficient SIFT matching

• We proposed a scheme to fast retrieval similar images.
• SIFT features are converted to binary strings to shorten the time needed for feature matching.
• A fast retrieval method using hash technique is proposed.
• The proposed scheme possesses corresponding distinguishing ability as compared to the original SIFT method.

The well-known SIFT is capable of extracting distinctive features for image retrieval. However, its matching is time consuming and slows down the entire process. In the SIFT matching, the Euclidean distance is used to measure the similarity of two features, which is expensive because it involves taking square root. Moreover, the scale of the image database is usually too large to adopt linear search for image retrieval. To improve the SIFT matching, this paper proposes a fast image retrieval scheme transforms the SIFT features to binary representations. The complexity of the distance calculation is reduced to bit-wise operation and the retrieval time is greatly decreased. Moreover, the proposed scheme utilizes hashing for retrieving similar images according to the binarized features and further speeds up the retrieval process. The experiment results show the proposed scheme can retrieve images efficiently with only a little sacrifice of accuracy as compared to SIFT.