A near-duplicate video detection method based on invariant moments and feature point matching

Tang You Chang, Min Yuan Guo, Shen-Chuan Tai, Guo Shiang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, a two-level near-duplicate video detection method based on invariant moment was proposed. To reduce the computational complexity of near-duplicate video detection, a coarse-to-fine approach was adopted in the proposed method. The proposed method is composed of key-frame selection, invariant moment calculation, feature point extraction and matching, similarity measurement, and near-duplicate classifier. After key-frame selection, the proposed method coarsely finds the corresponding frame pairs based on invariant moments. For each chosen frame pair, SURF is used to find the corresponding point pairs between the query frame and the test one. After feature-level, spatial-level, and temporal-level similarity measurement, we can decide whether the query video clip and the test one are near-duplicate. The experimental results show that the proposed method can effectively detect near-duplicate videos. In addition, the proposed method has good performance against possible operations, re-scaling, and frame-rate change.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
PublisherIOS Press
Number of pages10
ISBN (Electronic)9781614994831
Publication statusPublished - 2015
EventInternational Computer Symposium, ICS 2014 - Taichung, Taiwan
Duration: 2014 Dec 122014 Dec 14

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


OtherInternational Computer Symposium, ICS 2014

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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