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Near-Duplicate Video Detection Based on an Approximate Similarity Self-Join Strategy

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Abstract

The huge amount of redundant multimedia data, like video, has become a problem in terms of both space and copyright. Usually, the methods for identifying near-duplicate videos are neither adequate nor scalable to find pairs of similar videos. Similarity self-join operation could be an alternative to solve this problem in which all similar pairs of elements from a video dataset are retrieved. Nonetheless, methods for similarity self-join have poor performance when applied to high-dimensional data. In this work, we propose a new approximate method to compute similarity self-join in sub-quadratic time in order to solve the near-duplicate video detection problem. Our strategy is based on clustering techniques to find out groups of videos which are similar to each other.
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Dates and versions

hal-01305691 , version 1 (25-04-2016)

Identifiers

  • HAL Id : hal-01305691 , version 1

Cite

Henrique Batista da Silva, Zenilton Patrocino Jr., Guillaume Gravier, Laurent Amsaleg, Arnaldo de A. Araújo, et al.. Near-Duplicate Video Detection Based on an Approximate Similarity Self-Join Strategy. 14th International Workshop on Content-based Multimedia Indexing, Jun 2016, bucarest, Romania. ⟨hal-01305691⟩
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