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Single-threshold Image Segmentation Algorithm Based on Improved Bat Algorithm
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The Improved Bat Algorithm (IBA) is proposed for the image segmentation based on the maximum interclass variance method. Firstly, the principle of image segmentation based on the maximum interclass variance method is explained, and secondly, the bat algorithm is improved by using chaotic logistic mapping to initialize the population to improve the diversity of solutions, using adaptive parameter optimization to avoid falling into local optimum, using Monkey algorithm for individual selection, and finally, the image segmentation function in image segmentation is used as the individual fitness function of the bat algorithm for solving. The simulation experiments show that compared with the bat algorithm and the monkey group algorithm, this algorithm has better segmentation effect under different threshold values.
Title: Single-threshold Image Segmentation Algorithm Based on Improved Bat Algorithm
Description:
The Improved Bat Algorithm (IBA) is proposed for the image segmentation based on the maximum interclass variance method.
Firstly, the principle of image segmentation based on the maximum interclass variance method is explained, and secondly, the bat algorithm is improved by using chaotic logistic mapping to initialize the population to improve the diversity of solutions, using adaptive parameter optimization to avoid falling into local optimum, using Monkey algorithm for individual selection, and finally, the image segmentation function in image segmentation is used as the individual fitness function of the bat algorithm for solving.
The simulation experiments show that compared with the bat algorithm and the monkey group algorithm, this algorithm has better segmentation effect under different threshold values.
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