A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding

2008 ◽  
Vol 34 (2) ◽  
pp. 1341-1350 ◽  
Author(s):  
M MAITRA ◽  
A CHATTERJEE
Keyword(s):  
Image Segmentation ◽  
Pso Algorithm ◽  
Multilevel Thresholding

scholarly journals A Multilevel Thresholding Approach Based on Improved Particle Swarm Optimization for Color Image Segmentation

2020 ◽  
Author(s):  
Larissa Britto ◽  
Luciano Pacífico ◽  
Teresa Ludermir
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Color Image ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Color Image Segmentation ◽  
Multilevel Thresholding ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Image Problems

In this paper, a hybrid Otsu and improved Particle Swarm Optimization (PSO) algorithm is presented to deal with multilevel color image thresholding problem, named APSOW. In APSOW, the historical information represented by the local best solutions found so far by PSO population are permuted among the current population, using a randomized greedy process. APSOW also implements a weedout operator to prune the worst individuals from the population. The proposed APSOW is compared to other hybrid EAs and Otsu approaches from literature (include standard PSO model) through twelve benchmark color image problems, showing its potential and robustness.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

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