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Hybrid improved capuchin search algorithm for plant image thresholding.

Shujing Li1, Zhangfei Li1, Qinghe Li1

  • 1School of Computer and Information Engineering, Fuyang Normal University, Fuyang, China.

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|February 13, 2023
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Summary
This summary is machine-generated.

An improved Capuchin Search Algorithm (ICAPSA) enhances multi-level image segmentation by optimizing thresholds. This novel approach, using chaotic initialization and Levy Flight, outperforms existing methods in plant image analysis.

Keywords:
capuchin search algorithmchaotic mappinglevy flightopposite-based learningplant image thresholding

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Meta-heuristic optimization algorithms are increasingly used for multi-level image segmentation threshold optimization.
  • The original Capuchin Search Algorithm (CAPSA) faces challenges with initial population distribution, global search performance, and premature local optima.

Purpose of the Study:

  • To address the limitations of CAPSA in threshold optimization for multi-level image segmentation.
  • To propose and evaluate an improved Capuchin Search Algorithm (ICAPSA) with enhanced global search and local exploitation capabilities.

Main Methods:

  • Developed ICAPSA incorporating chaotic opposite-based learning for initial population quality.
  • Integrated Levy Flight disturbance strategy to balance global and local search during iteration.
  • Applied ICAPSA with Kapur's objective function for multi-level thresholding on plant images.

Main Results:

  • ICAPSA demonstrated superior performance in multi-level image segmentation compared to original CAPSA and other algorithms (FABC, DCOA, MWOA, ISBO).
  • The improved algorithm yielded better visual segmentation effects and data comparison outcomes.
  • Chaotic initialization and Levy Flight strategies effectively improved CAPSA's optimization capabilities.

Conclusions:

  • ICAPSA offers a significant advancement in meta-heuristic optimization for multi-level image segmentation.
  • The proposed improvements effectively mitigate issues of poor initial population distribution and local optima entrapment.
  • ICAPSA shows strong potential for accurate and efficient threshold optimization in image analysis applications.