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Competitive content-based video copy detection using global descriptors

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Abstract

Content-Based Video Copy Detection (CBVCD) consists of detecting whether or not a video document is a copy of some known original and to retrieve the original video. CBVCD systems rely on two different tasks: Feature Extraction task, that calculates many representative descriptors for a video sequence, and Similarity Search task, that is the algorithm for finding videos in an indexed collection that match a query video. This work details a CBVCD approach based on a combination of global descriptors, an automatic weighting algorithm, a pivot-based index structure, an approximate similarity search, and a voting algorithm for copy localization. This approach is analyzed using MUSCLE-VCD-2007 corpus, and it was tested at the TRECVID 2010 evaluation together with other state-of-the-art CBVCD systems. The results show that this approach enables global descriptors to achieve competitive results and even outperforms systems based on combination of local descriptors and audio information. This approach has a potential of achieving even higher effectiveness due to its seamless ability of combining descriptors from different sources at the similarity search level.

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Notes

  1. In particular, the system achieves recall 1 for ST1 and recall 90.5% for ST2. Despite EH10 descriptor is not invariant to mirroring, the system can detect a flipped copy from ST1 by just matching the mostly symmetrical frames.

  2. In fact, under the same conditions from Fig. 8 with segments of one second length, the detection performance for this example increases from recall 0.484 (15 detected copies) to 0.710 (22 detected copies) for precision 1.

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Authors and Affiliations

  1. PRISMA Research Group, Department of Computer Science, University of Chile, Av. Blanco Encalada 2120 3er Piso, 8370459, Santiago, Chile

    Juan Manuel Barrios & Benjamin Bustos

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  1. Juan Manuel Barrios
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  2. Benjamin Bustos
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Correspondence to Juan Manuel Barrios.

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Barrios, J.M., Bustos, B. Competitive content-based video copy detection using global descriptors. Multimed Tools Appl 62, 75–110 (2013). https://doi.org/10.1007/s11042-011-0915-x

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