Performance Characterization of 2D CNN Features for Partial Video Copy Detection

Abstract

2D CNN are main components for Partial Video Copy Detection (PVCD). 2D CNN features serve for the retrieval and matching of videos. Robustness is a key property of these features. It is a well-known problem in the computer vision field but little investigated for PVCD. The contributions of this paper are twofold: (i) based on a public video dataset, we provide large-scale experiments with 700 B of comparisons of 4.4 M feature vectors. We report conclusions for PVCD consistent with the state-of-the-art. (ii) the regular protocol for performance characterization is misleading for PVCD as it is bounded to the video level. A method for the characterization of key-frames with 2D CNN features is proposed. It is based on a goodness criterion and a time series modelling. It provides a fine categorization of key-frames and allows a deeper characterization of a PVCD problem with 2D CNN features.