Use of Support Vector Machines for Color Image Segmentation

2006 ◽  
Author(s):  
Omar Selmi ◽  
A. Pinti ◽  
Abdelmalik Taleb-Ahmed ◽  
Naceur Kerkeni
Keyword(s):  
Image Segmentation ◽  
Support Vector Machines ◽  
Color Image ◽  
Color Image Segmentation ◽  
Support Vector ◽  
Vector Machines

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.

COLOR IMAGE SEGMENTATION WITH SUPPORT VECTOR MACHINES: APPLICATIONS TO ROAD SIGNS DETECTION

2008 ◽  
Vol 18 (04) ◽  
pp. 339-345 ◽  
Author(s):  
BOGUSŁAW CYGANEK
Keyword(s):  
Input Data ◽  
Color Image ◽  
Feature Space ◽  
Recognition System ◽  
Color Image Segmentation ◽  
Support Vector ◽  
The Road ◽  
Road Signs ◽  
Higher Dimensional ◽  
Vector Machines

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

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