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Optimizing Multiple Entropy Thresholding by the Chaotic Combination Strategy Sparrow Search Algorithm for Aggregate

Mengfei Wang1, Weixing Wang1, Limin Li2

  • 1School of Information, Chang'an University, Xi'an 710064, China.

Entropy (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces PERSSA-MET, an improved image segmentation model for civil engineering. It enhances aggregate image analysis by accurately capturing surface details and edges, outperforming previous methods.

Keywords:
aggregate imagechaotic mapmultiple entropy thresholdingsparrow search algorithm

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Area of Science:

  • Civil Engineering
  • Image Processing
  • Computational Intelligence

Background:

  • Aggregate image analysis is crucial in civil engineering but current methods like Multiple Entropy Thresholding (MET) are inefficient.
  • Existing optimization strategies for MET lack accuracy, leading to loss of critical surface roughness and aggregate edge features in segmented images.

Purpose of the Study:

  • To develop an autonomous segmentation model (PERSSA-MET) that optimizes MET using an enhanced Sparrow Search Algorithm (SSA).
  • To improve the accuracy and detail retention in aggregate image segmentation for better analysis.

Main Methods:

  • Proposed PERSSA (Piecewise expansion parameter and range-control elite mutation strategy) to enhance SSA's accuracy for aggregate images with extreme values.
  • Utilized PERSSA to optimize MET thresholds (Renyi entropy, symmetric cross entropy, Kapur entropy).
  • Introduced Overall Merit Weight Proportion (OMWP) to evaluate segmentation performance.

Main Results:

  • PERSSA demonstrated superiority over seven other optimization algorithms in benchmark tests.
  • PERSSA-MET significantly improved segmentation accuracy, retaining more surface texture and edge details compared to SSA-MET.
  • Achieved notable improvements in Peak Signal-to-Noise Ratio (PSNR) by 28.90%, Structural Similarity Index Measure (SSIM) by 12.55%, and Feature Similarity Index Measure (FSIM) by 6.00%.

Conclusions:

  • PERSSA-MET, particularly PERSSA-Renyi entropy, effectively segments aggregate images, preserving crucial surface texture and edge features.
  • The proposed method achieves a superior balance between segmentation accuracy and computational speed.
  • PERSSA-MET represents a significant advancement in automated aggregate image analysis for civil engineering applications.