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Modified salp swarm algorithm based multilevel thresholding for color image segmentation.

Shi Kai Wang1, He Ming Jia2, Xiao Xu Peng2

  • 1School of Mathematical Sciences, Harbin Normal University, Harbin 150025, China.

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|November 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Salp Swarm Algorithm (SSA) for multi-threshold image segmentation, enhancing precision and outperforming other swarm algorithms in tests.

Keywords:
Kapur's entropy methodlevy flightmulti-threshold color image segmentationslap swarm optimization

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Multi-threshold image segmentation is crucial for image analysis but faces precision challenges with increasing thresholds.
  • Existing methods struggle to maintain accuracy as the number of segmentation thresholds grows.

Purpose of the Study:

  • To propose an improved multi-threshold image segmentation technique using a modified Salp Swarm Algorithm (SSA).
  • To enhance the precision of image segmentation by optimizing threshold selection.
  • To evaluate the effectiveness of the enhanced SSA in image segmentation tasks.

Main Methods:

  • A modified Salp Swarm Algorithm (SSA) incorporating Levy flight is developed for optimal parameter selection in image segmentation.
  • Kapur's entropy, Otsu's method, and Renyi entropy are employed as fitness functions during optimization.
  • The algorithm's performance is validated using standard reference images and compared against various swarm intelligence algorithms.

Main Results:

  • The proposed modified SSA demonstrates superior performance in multi-threshold image segmentation compared to other swarm algorithms.
  • Quantitative analysis using peak signal-to-noise ratio (PSNR) and feature similarity index measures (FSIM) confirms the algorithm's effectiveness.
  • The optimized threshold selection significantly improves segmentation accuracy.

Conclusions:

  • The modified Salp Swarm Algorithm (SSA) offers a robust and accurate solution for multi-threshold image segmentation.
  • Levy flight integration enhances the optimization capability of SSA for image segmentation tasks.
  • The proposed method provides a significant advancement in image segmentation precision and efficiency.