Automated clustering by support vector machines with a local-search strategy and its application to image segmentation

Optik ◽  
2015 ◽  
Vol 126 (24) ◽  
pp. 4964-4970 ◽  
Author(s):  
Chih-Hung Wu ◽  
Chih-Chin Lai ◽  
Chun-Yen Chen ◽  
Yan-He Chen
Keyword(s):  
Image Segmentation ◽  
Support Vector Machines ◽  
Local Search ◽  
Search Strategy ◽  
Support Vector ◽  
Vector Machines ◽  
Local Search Strategy

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.

Optimizing parameters of support vector machines using team-search-based particle swarm optimization

2015 ◽  
Vol 32 (5) ◽  
pp. 1194-1213 ◽  
Author(s):  
Long Zhang ◽  
Jianhua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Search Strategy ◽  
Cross Validation ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type ◽  
Vector Machines

Purpose – It is greatly important to select the parameters for support vector machines (SVM), which is usually determined by cross-validation. However, the cross-validation is very time-consuming and complicated to create good parameters for SVM. The parameter tuning issue can be solved in the optimization framework. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the authors propose a novel variant of particle swarm optimization (PSO) for the selection of parameters in SVM. The proposed algorithm is denoted as PSO-TS (PSO algorithm with team-search strategy), which is with team-based local search strategy and dynamic inertia factor. The ultimate design purpose of the strategy is to realize that the algorithm can be suitable for different problems with good balance between exploration and exploitation and efficiently control the inertia of the flight. In PSO-TS, the particles accomplish the assigned tasks according to different topology and detailedly search the achieved and potential regions. The authors also theoretically analyze the behavior of PSO-TS and demonstrate they can share the different information from their neighbors to maintain diversity for efficient search. Findings – The validation of PSO-TS is conducted over a widely used benchmark functions and applied to tuning the parameters of SVM. The experimental results demonstrate that the proposed algorithm can tune the parameters of SVM efficiently. Originality/value – The developed method is original.

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