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Image Annotation Using a Semantic Hierarchy

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Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11004))

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Abstract

With the fast development of smartphones and social media image sharing, automatic image annotation has become a research area of great interest. It enables indexing, extracting and searching in large collections of images in an easier and faster way. In this paper, we propose a model for the annotation extension of images using a semantic hierarchy. This latter is built from vocabulary keyword annotations combining a mixture of Bernoulli distributions with mixtures of Gaussians.

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Notes

  1. 1.

    https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz.

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Authors and Affiliations

  1. Université de Lorraine-LORIA, UMR 7503, Vandoeuvre-les-Nancy, France

    Abdessalem Bouzaieni & Salvatore Tabbone

Authors
  1. Abdessalem Bouzaieni
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  2. Salvatore Tabbone
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Corresponding author

Correspondence to Salvatore Tabbone .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Xiao Bai

  2. University of York, York, United Kingdom

    Edwin R. Hancock

  3. IBM Research – Thomas J. Watson Research, Yorktown Heights, New York, USA

    Tin Kam Ho

  4. University of York, Heslington, York, United Kingdom

    Richard C. Wilson

  5. University of Cagliari, Cagliari, Italy

    Battista Biggio

  6. Data 61 - CSIRO, Canberra, Aust Capital Terr, Australia

    Antonio Robles-Kelly

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Bouzaieni, A., Tabbone, S. (2018). Image Annotation Using a Semantic Hierarchy. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-97785-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97784-3

  • Online ISBN: 978-3-319-97785-0

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