An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A Time Warping Based Approach for Video Copy Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Spatio-temporal video object segmentation via scale-adaptive 3D structure tensor
EURASIP Journal on Applied Signal Processing
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Adaptive Subspace Symbolization for Content-Based Video Detection
IEEE Transactions on Knowledge and Data Engineering
Monitoring near duplicates over video streams
Proceedings of the international conference on Multimedia
Real-time large scale near-duplicate web video retrieval
Proceedings of the international conference on Multimedia
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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.