content-based video copy detection
Content-Based Video Copy Detection consists in retrieving all the modified versions of an original document in a video collection. It relies on two tasks: content description, for extracting one or many fingerprints from a video document, and similarity search, for determining the set of extracted fingerprints that make a close match. For the similarity search task, a copy detection system usually relies on a metric distance for measuring the degree of similarity between fingerprints. The metric properties represent a tradeoff between efficiency and effectiveness for a similarity search. A metric distance allows the use of well studied indexing structures. However, the metric properties restrict the similarity model that can be used for comparing two objects. For the present thesis, the main focus will be on researching similarity models for video sequences that do not necessarily comply the metric properties. In particular, we plan to research multi-metric and non-metric similarity measures for fulfilling effective and efficient detection. The issues involved in video copy detection (visual transformations, local and global fingerprints, temporal dimension, and approximated searches) make this problem a relevant topic for researching new similarity models.
content-based copy detection multi-metric spaces non-metric spaces