Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection

Near-duplicate (ND) detection appears as a timely issue recently, being regarded as a powerful tool for various emerging applications. In the Web 2.0 environment particularly, the identification of near-duplicates enables the tasks such as copyright enforcement, news topic tracking, image and video search. In this paper, we describe an algorithm, namely Scale-Rotation invariant Pattern Entropy (SR-PE), for the detection of near-duplicates in large-scale video corpus. SR-PE is a novel pattern evaluation technique capable of measuring the spatial regularity of matching patterns formed by local keypoints. More importantly, the coherency of patterns and the perception of visual similarity, under the scenario that there could be multiple ND regions undergone arbitrary transformations, respectively, are carefully addressed through entropy measure. To demonstrate our work in large-scale dataset, a practical framework composed of three components: bag-of-words representation, local keypoint matching and SR-PE evaluation, is also proposed for the rapid detection of near-duplicates.