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

Published: 28 November 2011 Publication History

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.

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Cited By

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  • (2017)Enhancing online video recommendation using social user interactionsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-017-0469-226:5(637-656)Online publication date: 1-Oct-2017
  • (2012)Structure Tensor Series-Based Large Scale Near-Duplicate Video RetrievalIEEE Transactions on Multimedia10.1109/TMM.2012.219448114:4(1220-1233)Online publication date: 1-Aug-2012

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    cover image ACM Conferences
    MM '11: Proceedings of the 19th ACM international conference on Multimedia
    November 2011
    944 pages
    ISBN:9781450306164
    DOI:10.1145/2072298
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 28 November 2011

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    Author Tags

    1. ASVT series
    2. near duplicates
    3. similarity measure

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    MM '11
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    MM '11: ACM Multimedia Conference
    November 28 - December 1, 2011
    Arizona, Scottsdale, USA

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    View all
    • (2017)Enhancing online video recommendation using social user interactionsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-017-0469-226:5(637-656)Online publication date: 1-Oct-2017
    • (2012)Structure Tensor Series-Based Large Scale Near-Duplicate Video RetrievalIEEE Transactions on Multimedia10.1109/TMM.2012.219448114:4(1220-1233)Online publication date: 1-Aug-2012

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