Robust voting algorithm based on labels of behavior for video copy detection

This paper presents an efficient approach for copies detection in a large videos archive consisting of several hundred of hours. The video content indexing method consists of extracting the dynamic behavior on the local description of interest points and further on the estimation of their trajectories along the video sequence. Analyzing the low-level description obtained allows to highlight trends of behaviors and then to assign a label of behavior to each local descriptor. Such an indexing approach has several interesting properties: it provides a rich, compact and generic description, while labels of behavior provide a high-level description of the video content. Here, we focus on video Content Based Copy Detection (CBCD). Copy detection is problematic as similarity search problem but with prominent differences. To be efficient, it requires a dedicated on-line retrieval method based on a specific voting function. This voting function must be robust to signal transformations and d...

• Type: Conference
• Year: 2006
• Where: MM
• Authors: Julien Law-To, Olivier Buisson, Valérie Gouet-Brunet, Nozha Boujemaa
• Keywords: Copy Detection, MM 2006, On-line Retrieval Method, Video Content