Near-duplicate video detection featuring coupled temporal and perceptual visual structures and logical inference based matching |
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Authors: | Mohammed Belkhatir Bashar Tahayna |
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Institution: | 1. Faculty of Computer Science, University of Lyon, Campus de la Doua, 69622 Villeurbanne Cedex, France;2. Faculty of Information Technology, Monash University, Sunway Campus, 46150, Malaysia |
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Abstract: | 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. |
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Keywords: | Near-duplicate video detection Perceptual visual indexing Logical inference Lattice-based processing Empirical evaluation |
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