Estimation of web video multiplicity

Research output: Contribution to journalConference articlepeer-review

31 Scopus citations

Abstract

With ever more popularity of video web-publishing, many popular contents are being mirrored, reformatted, modified and republished, resulting in excessive content duplication. While such redundancy provides fault tolerance for continuous availability of information, it could potentially create problems for multimedia search engines in that the search results for a given query might become repetitious, and cluttered with a large number of duplicates. As such, developing techniques for detecting similarity and duplication is important to multimedia search engines. In addition, content providers might be interested in identifying duplicates of their content for legal, contractual or other business related reasons. In this paper, we propose an efficient algorithm called video signature to detect similar video sequences for large databases such as the web. The idea is to first form a `signature' for each video sequence by selecting a small number of its frames that are most similar to a number of randomly chosen seed images. Then the similarity between any two video sequences can be reliably estimated by comparing their respective signatures. Using this method, we achieve 85% recall and precision ratios on a test database of 377 video sequences. As a proof of concept, we have applied our proposed algorithm to a collection of 1800 hours of video corresponding to around 45000 clips from the web. Our results indicate that, on average, every video in our collection from the web has around five similar copies.

Original languageEnglish
Pages (from-to)34-46
Number of pages13
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3964
StatePublished - 2000
EventProceedings of the 2000 Internet Imaging - San Jose, CA, USA
Duration: Jan 26 2000Jan 28 2000

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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