Near-duplicate video detection featuring coupled temporal and perceptual visual structures and logical inference based matching
This work addresses the problem of detecting near-duplicate videos for applications like re-broadcasted video search, offering an incremental improvement over existing dynamic programming techniques.
The paper tackles near-duplicate video detection by proposing an architecture that integrates temporal and perceptual visual features with logical inference-based matching, achieving robust performance against video editing and degradation, as demonstrated through empirical comparisons on TRECVID and real-world broadcast data.
We propose in this paper an architecture for near-duplicate video detection based on: (i) index and query signature based structures integrating temporal and perceptual visual features and (ii) a matching framework computing the logical inference between index and query documents. As far as indexing is concerned, instead of concatenating low-level visual features in high-dimensional spaces which results in curse of dimensionality and redundancy issues, we adopt a perceptual symbolic representation based on color and texture concepts. For matching, we propose to instantiate a retrieval model based on logical inference through the coupling of an N-gram sliding window process and theoretically-sound lattice-based structures. The techniques we cover are robust and insensitive to general video editing and/or degradation, making it ideal for re-broadcasted video search. Experiments are carried out on large quantities of video data collected from the TRECVID 02, 03 and 04 collections and real-world video broadcasts recorded from two German TV stations. An empirical comparison over two state-of-the-art dynamic programming techniques is encouraging and demonstrates the advantage and feasibility of our method.