Combination of Local and Global Features for Near-Duplicate Detection

This paper presents a new method to combine local and global features for near-duplicate images detection. It mainly consists of three steps. Firstly, the keypoints of images are extracted and preliminarily matched. Secondly, the matched keypoints are voted for estimation of affine transform to reduce false matching keypoints. Finally, to further confirm the matching, the Local Binary Pattern (LBP) and color histograms of areas formed by matched keypoints in two images are compared. This method has the advantage for handling the case when there are only a few matched keypoints. The proposed algorithm has been tested on Columbia dataset and compared quantitatively with the RANdom SAmple Consensus (RANSAC) and the Scale-Rotation Invariant Pattern Entropy (SR-PE) methods. The results turn out that the proposed method compares favorably against the state-of-the-arts.