Abstract
Barnacles mating optimizer (BMO) is an evolutionary algorithm that simulates the mating and reproductive behavior of barnacle population. In this article, an improved Barnacles mating optimizer based on logistic model and chaotic map (LCBMO) was proposed to produce the high-quality optimal result. Firstly, the logistic model is introduced into the native BMO to realize the automatic conversion parameters. This strategy maintains a proper relationship between exploitation and exploration. Then, the chaotic map is integrated to enhance the exploitation capability of the algorithm. After that, six variants based on LCBMO are compared to find the best algorithm on benchmark functions. Moreover, to the knowledge of the authors, there is no previous study on this algorithm for multilevel color image segmentation. LCBMO takes Masi entropy as the objective function to find the optimal threshold. By comparing different thresholds, different types of images, different optimization algorithms, and different objective functions, our proposed technique is reliable and promising in solving color image multilevel thresholding segmentation. Wilcoxon rank-sum test and Friedman test also prove that the simulation results are statistically significant.
Highlights
With the emergence of computer technology, image processing has been widely used in many fields
BERKELEY IMAGES SEGMENTATION EXPERIMENT This subsection analyzes the results provided by Masi entropy implementations based on CSA, GOA, CS, TLBO, EO, MPA, and LCBMO-2, after being applied to segment the 6 Berkeley images
WORK In this paper, the Barnacles mating optimizer algorithm based on logistic model and chaotic map for multilevel thresholding color image segmentation is proposed
Summary
With the emergence of computer technology, image processing has been widely used in many fields. A publication for multilevel thresholding segmentation of color satellite images based on Masi entropy has been proposed by Shubham in 2019. Simulation results show that the proposed method is effective and has better segmentation performance than Kapur, Renyi and Tsallis entropy [26]. Yang et al proposed chaos optimization algorithms based on chaotic maps to achieve the high efficiency, which improve the convergence speed and accuracy [36]. The experimental results confirm that the proposed Barnacles mating optimizer based on logistic model and chaotic map can be effectively used for multilevel thresholding.
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