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An improved golden jackal optimization for multilevel thresholding image segmentation.

Zihao Wang1, Yuanbin Mo2, Mingyue Cui1

  • 1School of Artificial Intelligence, Guangxi Minzu University, Nanning, China.

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Summary

This study introduces Helper Mechanism Based Golden Jackal Optimization (HGJO) for aerial image segmentation. HGJO improves segmentation accuracy by enhancing optimization algorithms and addressing image distortions.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Optimization Algorithms

Background:

  • Aerial photography provides valuable data but suffers from chromatic aberration and color distortion.
  • Effective image segmentation is crucial for enhancing features and simplifying subsequent processing in aerial imagery.

Purpose of the Study:

  • To develop an improved optimization algorithm for multilevel threshold segmentation of aerial images.
  • To enhance segmentation performance by addressing limitations of existing meta-heuristic algorithms.

Main Methods:

  • A novel algorithm, Helper Mechanism Based Golden Jackal Optimization (HGJO), is proposed.
  • HGJO incorporates opposition-based learning, an enhanced prey escape energy calculation, Cauchy distribution for exploration, and a helper mechanism to escape local optima.
  • The algorithm's performance is validated using the CEC2022 benchmark function test suite and applied to aerial image segmentation.

Main Results:

  • HGJO demonstrated competitive results on the CEC2022 benchmark test set compared to the original GJO and five other meta-heuristics.
  • Experiments showed that HGJO achieved superior segmentation results for aerial photography images compared to other tested algorithms.

Conclusions:

  • The proposed HGJO algorithm effectively improves multilevel threshold segmentation for aerial images.
  • HGJO offers enhanced performance in addressing optimization challenges and image quality issues in aerial photography.