Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering...

Area of Science:

• Computer Vision
• Image Processing
• Optimization Algorithms

Background:

• Image segmentation is crucial for feature extraction but challenging, especially for color images.
• Existing histogram-based methods lack spatial contextual information, limiting segmentation accuracy.
• Multilevel thresholding is a key technique for image segmentation.

Purpose of the Study:

• To propose a novel multilevel thresholding approach for color image segmentation.
• To enhance segmentation by incorporating spatial contextual information.
• To improve the efficiency and accuracy of image segmentation using optimization techniques.

Main Methods:

• A novel multilevel thresholding approach based on electromagnetism optimization (EMO) and an energy curve (MTEMOE) is introduced.
• Otsu's variance and Kapur's entropy are used as fitness functions to determine optimal threshold values.
• An energy curve is utilized to capture spatial relationships between neighboring pixels, overcoming histogram limitations.

Main Results:

• The proposed MTEMOE approach demonstrated superior performance in color image segmentation.
• Evaluations using metrics like MSE, PSNR, and SSIM showed significant improvements over other meta-heuristic algorithms.
• The method effectively utilized spatial contextual information for enhanced segmentation results.

Conclusions:

• The MTEMOE approach offers a significant advancement in color image segmentation.
• This method provides a robust solution for complex image segmentation tasks in various engineering fields.
• The integration of EMO and energy curves effectively addresses limitations of traditional segmentation techniques.