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, Volume 96, Issue 5, pp 381–402 | Cite as

Automatic image annotation approach based on optimization of classes scores

  • Nashwa El-Bendary
  • Tai-hoon Kim
  • Aboul Ella Hassanien
  • Mohamed Sami
Article

Abstract

This article presents an automatic image level annotation approach that takes advantage of both context and semantics presented in segmented images. The proposed approach is based on the optimization of classes’ scores using particle swarm optimization. In addition, random forest classifier and normalized cuts algorithm have been applied for automatic image classification, annotation, and clustering. For the proposed approach, each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. Two parameter selection models have been selected for particle swarm optimization algorithm and many voting techniques have been implemented to find the most suitable set of annotation words per image. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that applying optimization algorithms along with random forest classifier achieved noticeable increase in image annotation performance measures compared to related researches on the same dataset.

Keywords

Image annotation Segmentation Particle swarm optimization Random forest classifier 

Mathematics Subject Classification

68Uxx 

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Nashwa El-Bendary
    • 1
    • 2
  • Tai-hoon Kim
    • 3
  • Aboul Ella Hassanien
    • 4
    • 2
  • Mohamed Sami
    • 2
    • 4
  1. 1.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  2. 2.Scientific Research Group in Egypt (SRGE)CairoEgypt
  3. 3.Hannam UniversityDaejeonKorea
  4. 4.Faculty of Computers and InformationCairo UniversityCairoEgypt

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