Partial Near-Duplicate Detection in Random Images by a Combination of Detectors

Detection of partial near-duplicates (e.g. similar objects) in random images continues to be a challenging problem. In particular, scalability of existing methods is limited because keypoint correspondences have to be confirmed by the configuration analysis for groups of matched keypoints. We propose a novel approach where pairs of images containing partial near-duplicates are retrieved if ANY number of keypoint matches is found between both images (keypoint descriptions are augmented by some geometric characteristics of keypoint neighborhoods). However, two keypoint detectors (Harris-Affine and Hessian-Affine) are independently applied, and only results confirmed by both detectors are eventually accepted. Additionally, relative locations of keypoint correspondences retrieved by both detectors are analyzed and (if needed) outlines of the partial near-duplicates can be extracted using a keypoint-based co-segmentation algorithm. Altogether, the approach has a very low complexity (i.e. it is scalable to large databases) and provides satisfactory performances. Most importantly, precision is very high, while recall (determined primarily by the selected keypoint description and matching approaches) remains at acceptable level.