Near-duplicate web video retrieval and localization using improved edit distance

Hao Liu*, Qingjie Zhao, Hao Wang, Cong Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the development of network, there exists many nearduplicate videos online shared by individuals. These ones cause problems such as copyright infringement and search result redundancy. To solve the issues, this paper proposes a filter-and-refine framework for near-duplicate video retrieval and localization. By regarding video sequences as strings, Edit distance is used and improved in the approach. Firstly, bag-of-words (BOW) model is utilized to measure the similarities between frames. Then, non-near-duplicate videos are filtered out by computing the proposed relative Edit distance similarity (REDS). Next, a dynamic programming strategy is proposed to rank the remained videos and localize the similar segments. Experiments demonstrate the effectiveness and robustness of the method in retrieval and localization.

Original languageEnglish
Title of host publicationWeb Technologies and Applications - 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings
EditorsGuanfeng Liu, Feifei Li, Kyuseok Shim, Kai Zheng
PublisherSpringer Verlag
Pages141-152
Number of pages12
ISBN (Print)9783319458137
DOIs
Publication statusPublished - 2016
Event18th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2016 - Suzhou, China
Duration: 23 Sept 201625 Sept 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9931 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2016
Country/TerritoryChina
CitySuzhou
Period23/09/1625/09/16

Keywords

  • Edit distance
  • Near-duplicate video localization
  • Near-duplicate video retrieval

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