Detection and Segmentation of Near-duplicate Fragments in Random Images

Retrieval of near-duplicate image fragments is one of the most challenging problems is CBIR (content-based image retrieval). The objective is to identify almost the same fragments in random images of unpredictable contents. Such fragments usually represent identical object, though captured from a different viewpoint, under different photometric conditions and/or by a different camera. The paper presents techniques developed for such applications. In general, the proposed methods are based on statistical properties of keypoint similarities between compared images. In the first approach, we assume that near-duplicates are (approximately) related by affine transformations, i.e. the underlying objects are locally planar. In the second approach, a wider range of shape distortions is acceptable. Implementations (including online detection in real-time videos) are presented and their performances discussed. Additionally, an algorithm for a highly accurate segmentation of detected near-duplicate fragments is presented.