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Automatic Image Annotation Based on Low-Level Features and Classification of the Statistical Classes

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6743))

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Abstract

This work is devoted to the problem of automatic image annotation. This problem consists in assigning words of a natural language to an arbitrary image by analyzing textural characteristics (low-level features) of images without any other additional information. It can help to extract intellectual information from images and to organize searching procedures in a huge image base according to a textual query. We propose the general annotation scheme based on the statistical classes and their classification. This scheme consists in the following. First we derive the low-level features of images that can be presented by histograms. After that we represent these histograms by statistical classes and compute secondary features based on introduced inclusion measures of statistical classes. The automatic annotation is produced by aggregating secondary features using linear decision functions.

This work is supported by RFBR, projects #10-07-00135, #10-07-00478, #11-07-00591.

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Author information

Authors and Affiliations

  1. Mathematics Department, Technological Institute of Southern Federal University, Taganrog, Nekrasovskij Street 44, Taganrog, 347928, Russia

    Andrey Bronevich & Alexandra Melnichenko

Authors
  1. Andrey Bronevich
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  2. Alexandra Melnichenko
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Editor information

Editors and Affiliations

  1. State University Higher School of Economics, Pokrovskiy Bd. 11, 109028, Moscow, Russia

    Sergei O. Kuznetsov

  2. Institute of Mathematics, University of Warsaw, ul. Banacha 2, 02-097, Warsaw, Poland

    Dominik Ślęzak

  3. Department of Computer Science, University of Regina, 3737 Wascana Parkway, S4S 0A2, Regina, SK, Canada

    Daryl H. Hepting

  4. Birbeck University of London, Malet Street, WC1E 7HX, London, UK

    Boris G. Mirkin

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© 2011 Springer-Verlag Berlin Heidelberg

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Bronevich, A., Melnichenko, A. (2011). Automatic Image Annotation Based on Low-Level Features and Classification of the Statistical Classes. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_49

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  • DOI: https://doi.org/10.1007/978-3-642-21881-1_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21880-4

  • Online ISBN: 978-3-642-21881-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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