Detection of Near-Duplicate Patches in Random Images Using Keypoint-Based Features

Detection of similar fragments in unknown images is typically based on the hypothesize-and-verify paradigm. After the keypoint correspondences are found, the configuration constraints are used to identify clusters of similar and similarly transformed keypoints. This method is computationally expensive and hardly applicable to large databases. As an alternative, we propose novel affine-invariant TERM features characterizing geometry of groups of elliptical keyregions so that similar patches can be found by feature matching only. The paper overviews TERM features and reports experimental results confirming their high performances in image matching. A method combining visual words based on TERM descriptors with SIFT words is particularly recommended. Because of its low complexity, the proposed method can be prospectively used with visual databases of large sizes.