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Neighborhood Graphs for Semi-automatic Annotation of Large Image Databases

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Advances in Multimedia Modeling (MMM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4351))

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Abstract

Images annotation is the main tool for associating a semantic to an image. In this article we are interested in the semi-automatic annotation of images data. Indeed, with the great mass of data managed throughout the world and especially with the Web, the manual annotation of these images is almost impossible. We propose an approach based on neighborhood graphs offering several possibilities: content-based retrieval, key-words based interrogation, and the annotation which concerns us in this article. The approach we are proposing offers, as the experiments section shows it, very interesting annotation results while satisfying the scalability criteria which is a very significant point in this context where the mass of data is very important.

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Author information

Authors and Affiliations

  1. ERIC Laboratory, Lyon 2 University, 5, avenue Pierre Mendès-France, 69676, Bron cedex, France

    Hakim Hacid

Authors
  1. Hakim Hacid
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Editor information

Editors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Block N4, Nanyang Avenue, 639798, Singapore

    Tat-Jen Cham  & Deepu Rajan  & 

  2. School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Jianfei Cai

  3. IBM T.J. Watson Research Center, Yorktown Heights, P.O. Box 704, 10598, New York, USA

    Chitra Dorai

  4. National University of Singapore, 3 Science Dr, 117543, Singapore

    Tat-Seng Chua

  5. Center for Multimedia and Network Technology, School of Computer Enginnering, Nanyang Technological University, 639798, Singapore

    Liang-Tien Chia

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© 2006 Springer-Verlag Berlin Heidelberg

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Hacid, H. (2006). Neighborhood Graphs for Semi-automatic Annotation of Large Image Databases. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_57

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  • DOI: https://doi.org/10.1007/978-3-540-69423-6_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69421-2

  • Online ISBN: 978-3-540-69423-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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