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An Image-Based Approach to Video Copy Detection With Spatio -Temporal Post-Filtering

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. scaling 2. compression 3. cropping 4. camcording. FRAME INDEXING (step1~6).

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An Image-Based Approach to Video Copy Detection With Spatio -Temporal Post-Filtering

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  1. An Image-Based Approach to Video Copy Detection With Spatio-Temporal Post-Filtering MatthijsDouze, HervéJégou, and CordeliaSchmid, Senior Member, IEEE

  2. INTRODUCTION Common distortions are 1. scaling 2. compression 3. cropping 4. camcording

  3. FRAME INDEXING (step1~6) a. Frame Sampling 1. Uniform sampling 2. Keyframes

  4. b. Local Features (salient interest points) invariant : 1. Scale change 2. Image rotation 3. Noise c. Bag-of-Features and Hamming Embedding

  5. SPATIO-TEMPORAL VERIFICATION Spatio-Temporal Transformation Temporal Grouping Spatial Verification(next) Score Aggregation Strategy

  6. 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)

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

  8. Experiment Parameter Optimization(next) B. Handling of Trecvid Attacks C. TrecvidCopy Detection Results

  9. Conclusion Our video copy detection system outperformsother 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.

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