Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Video copy detection: a comparative study
Proceedings of the 6th ACM international conference on Image and video retrieval
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Scalable mining of large video databases using copy detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Accelerating near-duplicate video matching by combining visual similarity and alignment distortion
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Effective and Efficient Query Processing for Video Subsequence Identification
IEEE Transactions on Knowledge and Data Engineering
Automatic video tagging using content redundancy
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A suffix array approach to video copy detection in video sharing social networks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Scalable detection of partial near-duplicate videos by visual-temporal consistency
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Real-time large scale near-duplicate web video retrieval
Proceedings of the international conference on Multimedia
Efficient Mining of Multiple Partial Near-Duplicate Alignments by Temporal Network
IEEE Transactions on Circuits and Systems for Video Technology
An effective multi-clue fusion approach for web video topic detection
Proceedings of the 20th ACM international conference on Multimedia
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Robust and fast near-duplicate video detection is an important task with many potential applications. Most existing systems focus on the comparison between full copy videos or partial near-duplicate videos. While it is more challenging to find similar content for videos containing multiple near-duplicate segments at random locations with various connections. In this paper, we propose a new graph based method to detect complex near-duplicate video sub-clips. First, we develop a new succinct video descriptor for keyframe match. Then a graph is established to exploit temporal consistency of matched keyframes. The nodes of the graph are the matched frame pairs; the edge weights are computed from the temporal alignment and frame pair similarities. In this way, the validly matched keyframes would form a dense subgraph whose nodes are strongly connected. This graph model also preserves the complex connections of sub-clips. Thus detecting complex near-duplicate sub-clips is transformed to the problem of finding all the dense subgraphs. We employ the optimization method of graph shift to solve this problem due to its robust performance. The experiments are conducted on the dataset with various transformations and complex temporal relations. The results demonstrate the effectiveness and efficiency of the proposed method.