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Classification of images using Gaussian copula model in empirical cumulative distribution function space

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  • Sapto Wahyu Indratno
  • Sri Winarni
  • Kurnia Novita Sari

Abstract

This study introduces an innovative approach to image classification that uses Gaussian copulas with an Empirical Cumulative Distribution Function (ECDF) approach. The strategic use of distribution functions as feature descriptors simplifies the approach and enables a better understanding of the correlation structure between features in the image. This approach helps the model understand the contextual relationships between different parts of the image, resulting in a more abstract representation than a direct representation of individual pixel values. The proposed model utilizes the Distribution Function of the Distribution Value (DFDV) as the margin distribution. The Modified National Institute of Standards and Technology (MNIST) dataset is comprehensively used to assess the effectiveness of this model. The results show that the model achieves a noteworthy level of accuracy, with an average accuracy of 62.22% and a peak accuracy of 96.92%. This success was obtained by applying the Inference Function for Marginals (IFM) principles during the training stage.

Suggested Citation

  • Sapto Wahyu Indratno & Sri Winarni & Kurnia Novita Sari, 2024. "Classification of images using Gaussian copula model in empirical cumulative distribution function space," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0309884
    DOI: 10.1371/journal.pone.0309884
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    References listed on IDEAS

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    1. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
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