Open AccessSingle-threshold Image Segmentation Algorithm Based on Improved Bat Algorithm
Author(s) -
Wenqing Chen Wentan Jiao
Publication year - 2021
Publication title -
converter
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.104
H-Index - 1
ISSN - 0010-8189
DOI - 10.17762/converter.340
Subject(s) - image segmentation , bat algorithm , segmentation , fitness function , artificial intelligence , scale space segmentation , algorithm , segmentation based object categorization , pattern recognition (psychology) , image (mathematics) , computer science , population , mathematics , genetic algorithm , mathematical optimization , particle swarm optimization , demography , sociology
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.