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Partial-copy detection of non-simulated videos using learning at decision level

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Abstract

There is a renewed tendency to improve video copy detection tasks due to the involved challenges in non-simulated applications. In an adverse real-world scenario, the volume of data to process as well as the variety of transformations to which a video is exposed increases continuously. Moreover, the interest in detecting not only long videos but also short partial copies increments the difficulties in copy detection methods. Therefore, we propose a practical copy detection method able to cope with partial-copies and useful in applications where real-time processing is required. To accomplish the desirable characteristics of high precision, fast processing and scalability, we use low-cost global descriptors in combination with a decision strategy adapted from a reinforcement learning technique. Our evaluation results are satisfactory to detect short segments of at least 2-seconds length under a non-simulated and severely transformed video dataset.

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Notes

  1. The Content-Based Video Copy Detection task (CCD) was part of the TREC Video Retrieval (TRECVID) workshop series (from the year 2008 to 2011). See http://trecvid.nist.gov/

  2. The VCDB benchmark provided the VCDB baseline system timing data. They measured the processing time using a Xeon E5-2690 3.00 GHz CPU, using one thread without GPU intervention (see [16])

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Authors and Affiliations

  1. Science and Engineering Department, Universidad Iberoamericana, Puebla, Mexico

    Z. Jezabel Guzman-Zavaleta

  2. Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Sta. Ma. Tonanzintla, Puebla, México

    Claudia Feregrino-Uribe

Authors
  1. Z. Jezabel Guzman-Zavaleta
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  2. Claudia Feregrino-Uribe
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Correspondence to Z. Jezabel Guzman-Zavaleta.

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Guzman-Zavaleta, Z.J., Feregrino-Uribe, C. Partial-copy detection of non-simulated videos using learning at decision level. Multimed Tools Appl 78, 2427–2446 (2019). https://doi.org/10.1007/s11042-018-6345-2

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