Image Segmentation Based on Least Squares Support Vector Machines

Xin You Wang; Ya Li Ning; Xi Ping He

doi:10.4028/www.scientific.net/AMM.644-650.4314

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Image Segmentation Based on Least Squares Support...

Image Segmentation Based on Least Squares Support Vector Machines

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

4314-4318

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.4314

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Online since:

September 2014

Authors:

Xin You Wang*, Ya Li Ning, Xi Ping He

Keywords:

Color Image, Image Segmentation, Least Squares Support Vector Machines (Ls-SVM), Parameters Optimization

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References

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