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Performance Analysis of Differential Evolution Algorithm Variants in Solving Image Segmentation

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Computational Vision and Bio-Inspired Computing ( ICCVBIC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1108))

Abstract

Image segmentation is an activity of dividing an image into multiple segments. Thresholding is a typical step for analyzing image, recognizing the pattern, and computer vision. Threshold value can be calculated using histogram as well as using Gaussian mixture model. but those threshold values are not the exact solution to do the image segmentation. To overcome this problem and to find the exact threshold value, differential evolution algorithm is applied. Differential evolution is considered to be meta-heuristic search and useful in solving optimization problems. DE algorithms can be applied to process Image Segmentation by viewing it as an optimization problem. In this paper, Different Differential evolution (DE) algorithms are used to perform the image segmentation and their performance is compared in solving image segmentation. Both 2 class and 3-class segmentation is applied and the algorithm performance is analyzed. Experimental results shows that DE/best/1/bin algorithm out performs than the other variants of DE algorithms

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    V. SandhyaSree & S. Thangavelu

Authors
  1. V. SandhyaSree
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  2. S. Thangavelu
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Corresponding author

Correspondence to V. SandhyaSree .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, India

    S. Smys

  2. Faculty of Engineering, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. School of Computing, Tokyo Institute of Technology, Tokyo, Japan

    Abdullah M. Iliyasu

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✓ All authors declare that there is no conflict of interest.

✓ No humans/animals involved in this research work.

✓ We have used our own data.

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SandhyaSree, V., Thangavelu, S. (2020). Performance Analysis of Differential Evolution Algorithm Variants in Solving Image Segmentation. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_38

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