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Efficient search with multi-modality for video commercial retrieval

Published: 17 August 2013 Publication History

Abstract

Efficient and robust retrieval of commercial videos is an important topic for many applications such as commercial monitoring, market investigation. In this paper, we propose a two-step scheme to optimally incorporate the information of both visual and audio modalities into commercial retrieval. Firstly an efficient search method based on the extracted audio fingerprint feature is proposed to yield candidate results, and then visual signatures are extracted fused with audio features to validate the candidate results. The computational efficiency of audio modality and the robustness of visual modality are utilized simultaneously. The proposed optimal path search further guarantees the effectiveness in real applications. Comparison experiments were carried out over 123 video commercials and real TV programs from four channels. Results demonstrate both high efficiency and high robustness of the proposed method.

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Dalwon Jang, et al. 2006. Automatic Commercial Monitoring for TV Broadcasting Using Audio Fingerprinting. AES 29th International Conference,
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Guibin Zheng, J. H. 2006. Real-Time Audio Retrieval Method and Automatic Commercial Detecting System. Journal of Computer Science 2(3): 297--302.
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    ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
    August 2013
    419 pages
    ISBN:9781450322522
    DOI:10.1145/2499788
    • Conference Chair:
    • Tat-Seng Chua,
    • General Chairs:
    • Ke Lu,
    • Tao Mei,
    • Xindong Wu
    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]

    Sponsors

    • NSF of China: National Natural Science Foundation of China
    • University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
    • Beijing ACM SIGMM Chapter

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 August 2013

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    Author Tags

    1. efficient search
    2. multi-modality
    3. video commercial retrieval

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    • Research-article

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    ICIMCS '13
    Sponsor:
    • NSF of China
    • University of Sciences & Technology, Hefei

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    ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;
    Overall Acceptance Rate 163 of 456 submissions, 36%

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