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An Image-Based Approach to Video Copy Detection With
Spatio-Temporal Post-Filtering
Matthijs Douze, Hervé Jégou, and Cordelia Schmid, Senior
Member, IEEE
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INTRODUCTION
Common distortions are 1. scaling2. compression 3. cropping4. camcording
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FRAME INDEXING (step1~6)
a. Frame Sampling
1. Uniform sampling
2. Keyframes
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b. Local Features (salient interest
points)
invariant :
1. Scale change
2. Image rotation
3. Noise
c. Bag-of-Features and Hamming
Embedding
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SPATIO-TEMPORAL VERIFICATION
A. Spatio-Temporal Transformation
B. Temporal GroupingC. Spatial Verification(next)D.Score Aggregation
Strategy
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Spatial Verification 1. take all point matches from the matching frames. 2. estimate possible similarity transformations from all matching points with a Hough transform.(next)
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3. compute and score possible affine transformations. 4. select the maximum score over all possible hypotheses.
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ExperimentA. Parameter Optimization(next)B. Handling of Trecvid AttacksC. Trecvid Copy Detection Results
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Conclusion
Our video copy detection system outperforms other submitted results on all transformations. This is due to a very accurate image-level matching. Run KEYSADVES, which is more scalable, shows that our system still obtains excellent results with a memory footprint and query time reduced 20 times.