Modified particle swarm optimization-based multilevel thresholding for image segmentation

Liu, Yi; Mu, Caihong; Kou, Weidong; Liu, Jing

doi:10.1007/s00500-014-1345-2

Cited by 86 publications

(32 citation statements)

References 28 publications

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“…The basic principle of the two-dimensional maximum entropy method used in this study is to search (s, t) within the limited scope to make the two-dimensional entropy of area  and  in the two-dimensional histogram of the segmented image. Its computational formula [11] is:…”

Section: Image Segmentationmentioning

confidence: 99%

Threshold image target segmentation technology based on intelligent algorithms

Cai

Zhang

et al. 2020

Computer Optics

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This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.

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Section: Image Segmentationmentioning

confidence: 99%

Threshold image target segmentation technology based on intelligent algorithms

Cai

Zhang

et al. 2020

Computer Optics

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“…This generally leads to converging to the local optima due to the loss of diversity [ 22 ]. In such case, improving diversity becomes an important issue in PSO research [ 22 , 36 ]. As diversity is lost due to particles getting clustered together [ 37 ], adding disturbance to the particles is helpful for them to escape from the local optimal and enhance diversity.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation

Shen

Huang

Fan

2018

Sensors

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Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm.

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“…Compared with other evolutionary algorithms, PSO is easier to implement and has an excellent performance on various benchmark problems. As a result, PSO has been successfully applied in solving many realworld optimization problems (Eslami et al 2014;Liu et al 2014;Mazhoud et al 2013;Roy et al 2014;Tsai et al 2014;, Zhao et al 2014a, Idris et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Ecosystem particle swarm optimization

Liu

et al. 2016

Soft Comput

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Particle swarm optimization (PSO) is a wellknown swarm intelligence algorithm inspired by the foraging behavior of bird flocking. PSO has been widely used in many optimization and engineering problems due to its simplicity and efficiency, even though there still exist some disadvantages. The standard PSO often suffers with premature convergence or slow convergence when the optimization problem is multimodal or high-dimensional. To overcome these drawbacks, an ecosystem PSO (ESPSO) inspired by the characteristic that a natural ecosystem can excellently keep the biological diversity and make the whole ecosystem be in a dynamic balance is presented in this paper. ESPSO not only prevents the algorithm trapping into local optima but also balances the exploration and exploitation in both unimodal and multimodal problems as compared to other PSO variants. Twenty benchmark functions including unimodal functions and multimodal nonlinear functions are used to test the searching ability of ESPSO. Experimental results show that ESPSO considerably improves the searching accuracy, the algorithm reliability and the searching efficiency in comparison with other six well-known PSO variants and four evolutionary algorithms. Moreover, ESPSO was successfully Communicated by A. applied to the antenna array pattern synthesis design and gained satisfactory results.

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Modified particle swarm optimization-based multilevel thresholding for image segmentation

Cited by 86 publications

References 28 publications

Threshold image target segmentation technology based on intelligent algorithms

Threshold image target segmentation technology based on intelligent algorithms

Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation

Ecosystem particle swarm optimization

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