Efficient and robust detection of duplicate videos in a large database

  • Sarkar A
  • Singh V
  • Ghosh P
 et al. 
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Abstract

We present an efficient and accurate method for duplicate video detection in a large database using video fingerprints. We have empirically chosen the color layout descriptor, a compact and robust frame-based descriptor, to create fingerprints which are further encoded by vector quantization (VQ). We propose a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and experimentally show its superior performance over other distance measures for accurate duplicate detection. Efficient search cannot be performed for high-dimensional data using a nonmetric distance measure with existing indexing techniques. Therefore, we develop novel search algorithms based on precomputed distances and new dataset pruning techniques yielding practical retrieval times. We perform experiments with a database of 38 000 videos, worth 1600 h of content. For individual queries with an average duration of 60 s (about 50% of the average database video length), the duplicate video is retrieved in 0.032 s, on Intel Xeon with CPU 2.33 GHz, with a very high accuracy of 97.5%.

Author-supplied keywords

  • Color layout descriptor (CLD)
  • Duplicate detection
  • Nonmetric distance
  • Vector quantization (VQ)
  • Video fingerprinting

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Authors

  • Anindya SarkarUniversity of Calcutta

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  • Vishwarkarma Singh

  • Pratim Ghosh

  • Bangalore S. Manjunath

  • Ambuj Singh

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