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Rényi’s Entropy and Bat Algorithm Based Color Image Multilevel Thresholding

  • S. Pare
  • A. K. Bhandari
  • A. Kumar
  • G. K. Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

Colored satellite images are difficult to segment due to their low illumination, dense features, uncertainties, etc. Rényi’s entropy is a famous entropy criterion that provides excellent outputs in bi-level thresholding based segmentation. But such method suffers lack of accuracy, inefficiency, and instability when extended to perform color image multilevel thresholding. Therefore, a new color image multilevel segmentation strategy based on Bat algorithm and Rényi’s entropy is proposed in this paper to determine the optimal threshold values more efficiently. The experiments are conducted on four real satellite images and two well-known test images at different threshold levels. The study shows that the proposed algorithm obtains good quality and adequate segmented results more effectively as compared to other multilevel thresholding algorithms such as Rényi’s-PSO and Otsu-PSO.

Keywords

Color images Multilevel thresholding Rényi’s entropy Bat algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • S. Pare
    • 1
  • A. K. Bhandari
    • 2
  • A. Kumar
    • 1
  • G. K. Singh
    • 3
  1. 1.Indian Institute of Information Technology Design and Manufacturing, JabalpurJabalpurIndia
  2. 2.National Institute of Technology, PatnaPatnaIndia
  3. 3.Indian Institute of Technology RoorkeeRoorkeeIndia

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