Video Copy Detection: a Comparative Study

Julien Law-To (1) , Li Chen (2) , Alexis Joly (3) , Ivan Laptev (4) , Olivier Buisson (5) , Valérie Gouet-Brunet (3) , Nozha Boujemaa (3) , Fred Stentiford (2)

1 Exalead
2 UCL - University College of London [London]
3 IMEDIA - Image and multimedia indexing, browsing and retrieval
4 VISTA - Vision spatio-temporelle et active
5 INA-SUP

This paper presents a comparative study of methods for video copy detection. Different state-of-the-art techniques, using various kinds of descriptors and voting functions, are described: global video descriptors, based on spatial and temporal features; local descriptors based on spatial, temporal as well as spatio-temporal information. Robust voting functions is adapted to these techniques to enhance their performance and to compare them. Then, a dedicated framework for evaluating these systems is proposed. All the techniques are tested and compared within the same framework, by evaluating their robustness under single and mixed image transformations, as well as for different lengths of video segments. We discuss the performance of each approach according to the transformations and the applications considered. Local methods demonstrate their superior performance over the global ones, when detecting video copies subjected to various transformations.

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Multimédia [cs.MM]; Apprentissage [cs.LG]; Modélisation et simulation; Biodiversité et Ecologie; Environnement et Société

Julien Law-To, Li Chen, Alexis Joly, Ivan Laptev, Olivier Buisson, et al.. Video Copy Detection: a Comparative Study. CIVR 2007, Jul 2007, Amsterdam, France. ⟨hal-02420846⟩