An Image-Based Approach to Video Copy Detection With

Spatio-Temporal Post-Filtering

Matthijs Douze, Hervé Jégou, and Cordelia Schmid, Senior

Member, IEEE

INTRODUCTION

Common distortions are 1. scaling2. compression 3. cropping4. camcording

FRAME INDEXING (step1~6)

a. Frame Sampling

1. Uniform sampling

2. Keyframes

b. Local Features (salient interest

points)

invariant :

1. Scale change

2. Image rotation

3. Noise

c. Bag-of-Features and Hamming

Embedding

SPATIO-TEMPORAL VERIFICATION

A. Spatio-Temporal Transformation

B. Temporal GroupingC. Spatial Verification(next)D.Score Aggregation

Strategy

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)

3. compute and score possible affine transformations. 4. select the maximum score over all possible hypotheses.

ExperimentA. Parameter Optimization(next)B. Handling of Trecvid AttacksC. Trecvid Copy Detection Results

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.