Z-grid-based probabilistic retrieval for scaling up content-based copy detection

Scalability is the key issue in making content-based copy de-tection (CBCD) methods practical for very large image and video databases. Since copies are transformed versions of original documents, CBCD involves some form of retrieval by similarity using as queries the descriptions of potential copies. To enhance the scalability of an existing competitive CBCD method, we introduce here three improvements of this retrieval process: a Z-grid for building the index, uniformity-based sorting and adapted partitioning of the components. Retrieval speed is significantly increased, enabling us to monitor with a single computer one TV channel against a database of 120,000 hours of video.