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

Henrique Batista Da Silva 1 Zenilton Patrocino Jr. 2 Guillaume Gravier 3 Laurent Amsaleg 3 Arnaldo De A. Araújo 1 Silvio Jamil Guimarães 2
1 UFMG - Universidade Federal de Minas Gerais [Belo Horizonte]
2 Pontifical Catholic University of Minas Gerais [Belo Horizonte]
3 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
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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Communication dans un congrès
14th International Workshop on Content-based Multimedia Indexing, Jun 2016, bucarest, Romania
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Contributeur : Laurent Amsaleg <>
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Dernière modification le : jeudi 15 novembre 2018 - 11:58:51
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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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