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Image Feature Extraction Using Symbolic Data of Cumulative Distribution Functions

Author

Listed:
  • Sri Winarni

    (Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

  • Sapto Wahyu Indratno

    (Statistics Research Group, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung 40132, Indonesia)

  • Restu Arisanti

    (Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

  • Resa Septiani Pontoh

    (Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

Abstract

Symbolic data analysis is an emerging field in statistics with great potential to become a standard inferential technique. This research introduces a new approach to image feature extraction using the empirical cumulative distribution function (ECDF) and distribution function of distribution values (DFDV) as symbolic data. The main objective is to reduce the dimension of huge pixel data by organizing them into more coherent pixel-intensity distributions. We propose a partitioning method with different breakpoints to capture pixel intensity variations effectively. This results in an ECDF representing the proportion of pixel intensities and a DFDV representing the probability distribution at specific points. The novelty of this approach lies in using ECDF and DFDV as symbolic features, thus summarizing the data and providing a more informative representation of the pixel value distribution, facilitating image classification analysis based on intensity distribution. The experimental results underscore the potential of this method in distinguishing image characteristics among existing image classes. Image features extracted using this approach promise image classification analysis with more informative image representations. In addition, theoretical insights into the properties of DFDV distribution functions are gained.

Suggested Citation

  • Sri Winarni & Sapto Wahyu Indratno & Restu Arisanti & Resa Septiani Pontoh, 2024. "Image Feature Extraction Using Symbolic Data of Cumulative Distribution Functions," Mathematics, MDPI, vol. 12(13), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2089-:d:1428292
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    References listed on IDEAS

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    1. M. Vrac & L. Billard & E. Diday & A. Chédin, 2012. "Copula analysis of mixture models," Computational Statistics, Springer, vol. 27(3), pages 427-457, September.
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