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Automatic image annotation approach based on optimization of classes scores

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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.

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Authors and Affiliations

  1. Arab Academy for Science, Technology, and Maritime Transport, Cairo, Egypt

    Nashwa El-Bendary

  2. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Nashwa El-Bendary, Aboul Ella Hassanien & Mohamed Sami

  3. Hannam University, Daejeon, Korea

    Tai-hoon Kim

  4. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien & Mohamed Sami

Authors
  1. Nashwa El-Bendary
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  2. Tai-hoon Kim
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  3. Aboul Ella Hassanien
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  4. Mohamed Sami
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Correspondence to Aboul Ella Hassanien.

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El-Bendary, N., Kim, Th., Hassanien, A.E. et al. Automatic image annotation approach based on optimization of classes scores. Computing 96, 381–402 (2014). https://doi.org/10.1007/s00607-013-0342-0

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  • DOI: https://doi.org/10.1007/s00607-013-0342-0

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