Image Segmentation Based on Bio-Inspired Optimization Algorithms

2015 ◽  
pp. 259-284
Author(s):  
Hongwei Mo ◽  
Lifang Xu ◽  
Mengjiao Geng
Keyword(s):  
Image Processing ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimization Algorithms ◽  
Objective Criterion ◽  
Swarm Optimization ◽  
Fish School ◽  
Bacterial Foraging Algorithm ◽  
Search Capability

This chapter addresses the issue of image segmentation by clustering in the domain of image processing. Fuzzy C-Means is a widely adopted clustering algorithm. Bio-inspired optimization algorithms are optimal methods inspired by the principles or behaviors of biology. For the purpose of reinforcing the global search capability of FCM, five Bio-Inspired Optimization Algorithms (BIOA) including Biogeography-Based Optimization (BBO), Artificial Fish School Algorithm (AFSA), Artificial Bees Colony (ABC), Particle Swarm Optimization (PSO), and Bacterial Foraging Algorithm (BFA) are used to optimize the objective criterion function, which is interrelated to centroids in FCM. The optimized FCMs by the five algorithms are used for image segmentation, respectively. They have different effects on the results.

Image Segmentation Based on Bacterial Foraging and FCM Algorithm

2013 ◽  
pp. 209-222
Author(s):  
Hongwei Mo ◽  
Yujing Yin
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Algorithm ◽  
Data Sets ◽  
Bacterial Foraging Optimization ◽  
Objective Criterion ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Search Capability ◽  
Bacterial Foraging Optimization Algorithm

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

Image Segmentation Based on Bacterial Foraging and FCM Algorithm

2011 ◽  
Vol 2 (3) ◽  
pp. 16-28
Author(s):  
Hongwei Mo ◽  
Yujing Yin
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Algorithm ◽  
Data Sets ◽  
Criterion Function ◽  
Bacterial Foraging Optimization ◽  
Objective Criterion ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Search Capability

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

Study of Relative Entropy Coefficients for Image Segmentation Based on Particle Swarm Optimization

2011 ◽  
Vol 58-60 ◽  
pp. 1056-1060
Author(s):  
You Rui Huang ◽  
Li Guo Qu
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Relative Entropy ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Threshold Method ◽  
Swarm Optimization ◽  
Computation Algorithm

Image segmentation is the basis of image analysis, and because of its simplicity, rapidity and stability, the threshold method is the important one, applying in the image processing and recognition widely. In this paper, a new method is proposed, which based on relative entropy coefficients between random variables. It maximizes the target and background, which is the relative entropy coefficient in probability distribution, and gets the optimal threshold of image segmentation, and then optimizes it using particle swarm algorithm which is an evolutionary computation algorithm. The result of relative entropy coefficients for image segmentation proves its feasibility and better effect.

scholarly journals Multi-Level Thresholding for Image Segmentation With Swarm Optimization Algorithms

2017 ◽  
Vol 3 (3) ◽  
pp. 1 ◽  
Author(s):  
Murat Karakoyun ◽  
Nurdan Akhan Baykan ◽  
Mehmet Hacibeyoglu
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Threshold Value ◽  
Threshold Values ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Multi Level

Image segmentation is an important problem for image processing. The image processing applications are generally affectedfromthe segmentation success. There is noany image segmentation method which gives good results for all sorts of images. That’s why there are many approaches and methods forimage segmentationin the literature. And one of the most used is the thresholding technique. Thresholding techniques can be categorized into two topics: bi-level and multi-level thresholding. Bi-level thresholding technique has one threshold value which separates the image into two groups. However, multi-level thresholding technique uses n threshold values where n greater than one. In this paper, two swarm optimization algorithms (Particle Swarm Optimization, PSO and Cat Swarm Optimization, CSO) are applied on finding the optimum threshold values for the multi-level thresholding. In literature, there are some minimization or maximization functions to find the best threshold values for thresholding problem. Some of these methods are: Tsalli’s Entropy, Kapur’s Entropy, Renyi’s Entropy, Otsu’s Method (within class variance/between class variance), the Minimum Cross Entropy Thresholding (MCET) etc.In this work, Otsu’s (within class variance) method, which is one of these popular functions,is used as the fitness function of algorithms.In the experiments, five real images are segmented by usingParticle Swarm Algorithm and Cat Swarm Optimization Algorithms. The performances of the swarm algorithms on multi-level thresholding problem arecompared with Peak Signal-to-Noise Ratio (PSNR) and fitness function (FS) values. As a result, the PSO yields better performance than CSO.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

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