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Efficient Summarizing of Multimedia Archives Using Cluster Labeling

  • Conference paper
Image and Video Retrieval (CIVR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4071))

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Abstract

In this demo we present a novel approach for labeling clusters in minimally annotated data archives. We propose to build on clustering by aggregating the automatically tagged semantics. We propose and compare four techniques for labeling the clusters and evaluate the performance compared to human labeled ground-truth. We define the error measures to quantify the results, and present examples of the cluster labeling results obtained on the BBC stock shots and broadcast news videos from the TRECVID-2005 video data set.

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Author information

Authors and Affiliations

  1. IBM T. J. Watson Research Center, Hawthorne, NY, 10532, USA

    Jelena Tešić & John R. Smith

Authors
  1. Jelena Tešić
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  2. John R. Smith
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Editor information

Editors and Affiliations

  1. Arts, Media and Engineering Program, Arizona State University, 85281, Tempe, AZ,  

    Hari Sundaram

  2. Intelligent Information Management Department, IBM T.J. Watson Research Center, 19 Skyline Drive, 10532, Hawthorne, NY, USA

    Milind Naphade

  3. Intelligent Information Management Department, IBM T. J. Watson Research Center, 19 Skyline Drive, 10532, Hawthorne, NY, USA

    John R. Smith

  4. Microsoft Corporation, Microsoft China R&D Group, 49 Zhichun Road, 100080, Beijing, China

    Yong Rui

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© 2006 Springer-Verlag Berlin Heidelberg

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Tešić, J., Smith, J.R. (2006). Efficient Summarizing of Multimedia Archives Using Cluster Labeling. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_58

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  • DOI: https://doi.org/10.1007/11788034_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36018-6

  • Online ISBN: 978-3-540-36019-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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