Labeling Complementary Local Descriptors Behavior for Video Copy Detection

Abstract

This paper proposes an approach for indexing large collections of videos, dedicated to content-based copy detection. The visual description chosen involves local descriptors based on interest points. Firstly, we propose the joint use of different natures of spatial supports for the local descriptors. We will demonstrate that this combination provides a more representative and then a more informative description of each frame. As local supports, we use the classical Harris detector, added to a detector of local symmetries which is inspired by pre-attentive human vision and then expresses a strong semantic content. Our second contribution consists in enriching such descriptors by characterizing their dynamic behavior in the video sequence: estimating the trajectories of the points along frames allows to highlight trends of behaviors, and then to assign a label of behavior to each local descriptor. The relevance of our approach is evaluated on several hundred hours of videos, with severe attacks. The results obtained clearly demonstrate the richness and the compactness of the new spatio-temporal description proposed.