Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fast and robust short video clip search using an index structure
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Robust voting algorithm based on labels of behavior for video copy detection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A fast video copy detection approach by dynamic programming
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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Video copy detection is an active research field in copyright control, business intelligence and advertisement monitor etc. The main issues are transformation-invariant feature extraction and robust registration in object level. This paper proposes a novel video copy detection approach based on spatial-temporal-scale registration. In detail, we first build interesting points' trajectories by speeded up robust features (SURF). Then we use an efficient voting based spatial-temporal-scale registration approach to estimate the optimal transformation parameters and achieve the final video copy detection results by propagations of video segments in both spatial-temporal and scale directions. To speed up the detection speed, we use local sensitive hash indexing (LSH) to index trajectories for fast queries of candidate trajectories. Compared with existing approaches, our approach can detect many kinds of copy transformations including cropping, zoom in/out, camcording and re-encoding etc. Extensive experiments on 200 hours of videos demonstrate the effectiveness of our approach.