Mean shift-based SIFT keypoint filtering for region-of-interest determination

This paper presents an improved keypoint filtering method for region-of-interest (ROI) determination. Mean shift-based clustering was employed to group the scale invariant feature transform (SIFT) keypoints that appeared in the nearest region to get more locality. The proposed method uses the location of the extracted SIFT keypoints for grouping, and an average SIFT descriptor is calculated on the clustered keypoints. The support vector machine (SVM) classifies the average SIFT descriptor as an artificial or a natural keypoint. After the keypoint classification, only the keypoints classified as artificial keypoints by the binary SVM are used in near-duplicate detection (NDD). Finally, we determine the ROI using the adaptive selection of orientation histogram and the elimination of isolated keypoints. According to the result of experiments on keypoint classification, NDD and ROI determination, the proposed method obtained improved results compared to the previous methods.