Image Segmentation Based on NSCT and BF-PSO Algorithm

2013 ◽  
pp. 303-311
Author(s):  
Le Wang ◽  
Zhenbing Zhao
Keyword(s):  
Image Segmentation ◽  
Pso Algorithm

Image Segmentation Based on Least Squares Support Vector Machines

2014 ◽  
Vol 644-650 ◽  
pp. 4314-4318
Author(s):  
Xin You Wang ◽  
Ya Li Ning ◽  
Xi Ping He
Keyword(s):  
Image Segmentation ◽  
Support Vector Machines ◽  
Least Squares ◽  
Color Image ◽  
Pso Algorithm ◽  
Kernel Functions ◽  
Color Image Segmentation ◽  
Support Vector ◽  
Standard Data ◽  
Vector Machines

In order to solve the problem of the conventional methods operated directly in the image, difficult to obtain good results because they are poor in high dimension performance. In this paper, a new method was proposed, which use the Least Squares Support Vector Machines in image segmentation. Furthermore, the parameters of kernel functions are also be optimized by Particle Swarm Optimization (PSO) algorithm. The practical application in various of standard data sets and color image segmentation experiment. The results show that, LS-SVM can use a variety of features in image, the experiments have achieved good results of image segmentation, and the time needed for segmentation is greatly reduced than standard SVM.

A new social and momentum component adaptive PSO algorithm for image segmentation

2011 ◽  
Vol 38 (5) ◽  
pp. 4998-5004 ◽  
Author(s):  
Akhilesh Chander ◽  
Amitava Chatterjee ◽  
Patrick Siarry
Keyword(s):  
Image Segmentation ◽  
Pso Algorithm ◽  
Momentum Component ◽  
Adaptive Pso

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

2017 ◽  
pp. 106-142 ◽  
Author(s):  
Sourav De ◽  
Firoj Haque
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Optimization Technique ◽  
Real Life ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gray Scale ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Segmented Images

Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

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