Multimodal video copy detection applied to social media

  • Authors:

  • Xavier Anguera;Pere Obrador;Nuria Oliver

  • Affiliations:

  • Telefonica Research, Barcelona, Spain;Telefonica Research, Barcelona, Spain;Telefonica Research, Barcelona, Spain

  • Venue:
  • WSM '09 Proceedings of the first SIGMM workshop on Social media
  • Year:
  • 2009

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Abstract

Reliable content-based copy detection algorithms (CBCD) are at the core of effective multimedia data management and copyright enforcement systems. CBCD techniques focus on detecting videos that are identical to or transformed versions of an original video. The fast growth of online video sharing services challenges state-of-the-art copy detection algorithms as they need to be: able to deal with vast amounts of data, computationally efficient and robust to a wide range of image and audio transformations. In this paper, we present two related multimodal CBCD algorithms that effectively fuse audio and video information by means of a compact multimodal signature based on audio and video global descriptors. We validate our algorithms with a benchmark database (MUSCLE-VCD) and obtain over a 14% relative improvement with respect to state-of-the-art systems. In addition, we illustrate the performance of our approach in a video view-count re-ranking task with YouTube data.