Abstract
Near duplicate video retrieval has attracted much attention due to its wide spectrum of applications including copyright detection, commercial monitoring and news video tracking. In recent years, there has been significant research effort on efficiently identifying near duplicates from large video collections. However, existing approaches for large video databases suffer from low accuracy due to the serious information loss. In this paper, we propose a practical solution based on 3D structure tensor model for this problem. We first propose a novel video representation scheme, adaptive structure video tensor series (ASVT series), together with a robust similarity measure, to improve the retrieval effectiveness. Then, we prove the effectiveness of the proposed method by extensive experiments on hundreds hours real video data.
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