# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
###### Abstract  
In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous monotone decreasing individual learning factor are used to balance global and local search. Finally, chaos optimization is introduced to increase the diversity of the population to achieve OTSU multi-threshold image segmentation based on improved particle swarm optimization (IPSO). Twelve benchmark functions are selected to test the performance of the algorithm and are compared with the traditional meta-heuristic algorithm. The results show the robustness and superiority of the algorithm. The standard dataset images are used for multi-threshold image segmentation experiments, and some traditional meta-heuristic algorithms are selected to compare the calculation efficiency, peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and fitness value (FTINESS). The results show that the running time of this paper is 30% faster than other algorithms in general, and the accuracy is also better than other algorithms. Experiments show that the proposed algorithm can achieve higher segmentation accuracy and efficiency. threshold segmentation; OTSU method; particle swarm optimization; adaptability function +
Footnote †: journal: Applied Sciences  
Recently, [PERSON] et al. proposed an improved balance optimizer to solve the problem of imbalance between the exploration stage and the development stage of the balance optimizer, which solves the problem that the algorithm optimization is easy to fall into local optimization and shows the excellent performance of this algorithm [8]. [PERSON], [PERSON], and others improved the original butterfly optimization algorithm by combining the mutualism and parasitic stages of the traditional butterfly algorithm and the symbiotic biological search algorithm. Using kapur entropy as the fitness function, they selected a group of benchmark images to find the optimal threshold. The results show that the improved butterfly algorithm is superior to other algorithms in all evaluation indicators [9]. In addition, [PERSON] et al., in view of the shortcomings of the Harris Hawks Optimizer algorithm, proposed an improved version of the HHO algorithm, which solved the problem of poor search ability of traditional algorithms and makes it easy to fall into local optimization [10]. [PERSON] et al. proposed an improved PSO algorithm to solve the problem of premature convergence of traditional PSO and effectively realize adaptive image segmentation [11]. [PERSON] et al. proposed a cross strategy-based ant colony algorithm (CCACO) in order to solve the continuity problem of the ant colony algorithm, which uses Kapur entropy as the objective function for image segmentation. Experiments show that the proposed CCACO achieved excellent segmentation results in both low-threshold and high-threshold [12]. Of course, in the process of image segmentation, it is far from enough to rely only on threshold segmentation. Because of the low processing speed and segmentation accuracy of threshold segmentation, people will use optimization algorithms to optimize the segmentation process. Among many optimization algorithms, the meta-heuristic optimization algorithm is widely used because of its low cost, high accuracy, and fast speed.