Indexing local configurations of features for scalable content-based video copy detection

Content-based video copy detection is relevant for structuring large video databases. The use of local features leads to good robustness to most types of photometric or geometric transformations. However, to achieve both good precision and good recall when the transformations are strong, feature configurations should be taken into account. This usually leads to complex matching operations that are incompatible with scalable copy detection. We suggest a computationally inexpensive solution for including a minimal amount of configuration information that significantly improves the balance between overall detection quality and scalability.

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