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Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning.

Qiang Geng1,2, Huifeng Yan3

  • 1School of Big Data & Software Engineering, Chongqing College of Mobile Communication, Chongqing 401520, China.

Computational Intelligence and Neuroscience
|April 4, 2022
PubMed
Summary
This summary is machine-generated.

This study enhances image segmentation efficiency by fusing the Krill Herd algorithm with graph segmentation. The improved method significantly reduces processing time and boosts accuracy, especially with deep learning models like DC-Unet.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Traditional threshold-based image segmentation methods can be inefficient and lack accuracy for complex datasets.
  • Machine learning models offer potential for improved image segmentation but require efficient training and optimization.

Purpose of the Study:

  • To enhance the efficiency and accuracy of image segmentation.
  • To compare traditional threshold-based methods with machine learning approaches.
  • To develop a novel graph segmentation algorithm by integrating the Krill Herd optimization algorithm.

Main Methods:

  • A new graph segmentation algorithm was developed by combining the Krill Herd optimization algorithm with the maximum between-class variance function.
  • The algorithm was trained and validated using the pet dataset to build an image semantic segmentation system.
  • Performance was evaluated against traditional methods (Ostu algorithm) and deep learning models (Unet, SegNet, DC-Unet).

Main Results:

  • The improved Krill Herd algorithm reduced iterations by 6.95 times for single-threshold segmentation and execution time by approximately 0.24s for double-threshold segmentation compared to Ostu.
  • Deep learning models achieved high accuracy: Unet (86.3%), SegNet (91.9%), and DC-Unet (93.1%).
  • The proposed fusion algorithm demonstrated high optimization efficiency and practicality for multi-threshold image segmentation.

Conclusions:

  • The fusion algorithm offers a significant improvement in efficiency and accuracy for image segmentation.
  • The DC-Unet network model shows particular strength in segmenting fine image details.
  • This research presents a novel approach for developing efficient and accurate image segmentation methods.