Structure tensor series-based matching for near-duplicate video retrieval

Xiangmin Zhou*, Lei Chen, Xiaofang Zhou*

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Near duplicate video retrieval has attracted much attention due to its wide spectrum of applications including copyright detection, commercial monitoring and news video tracking. In recent years, there has been significant research effort on efficiently identifying near duplicates from large video collections. However, existing approaches for large video databases suffer from low accuracy due to the serious information loss. In this paper, we propose a practical solution based on 3D structure tensor model for this problem. We first propose a novel video representation scheme, adaptive structure video tensor series (ASVT series), together with a robust similarity measure, to improve the retrieval effectiveness. Then, we prove the effectiveness of the proposed method by extensive experiments on hundreds hours real video data.

Original languageEnglish
Title of host publicationMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
Pages1057-1060
Number of pages4
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 - Scottsdale, AZ, United States
Duration: 28 Nov 20111 Dec 2011

Publication series

NameMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops

Conference

Conference19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11
Country/TerritoryUnited States
CityScottsdale, AZ
Period28/11/111/12/11

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