Title :
Use of Support Vector Machines for Color Image Segmentation
Author :
Selmi, Omar ; Pinti, A. ; Taleb-Ahmed, Abdelmalik ; Kerkeni, Naceur
Author_Institution :
Univ. de Valenciennes et du Hainaut Cambresis
Abstract :
We present in this document the first results of our work concerning the implementation of a non-supervised classification algorithm based on support vector machines (SVM) for color image segmentation. The principle of the technique consists in submitting the RGB color attributes (red, green and blue) of the picture to the algorithm of classification to determine the zones that have the same color and therefore determine the different present objects in the image. Our main contribution is the use of a classification algorithm based on support vector machines for image segmentation
Keywords :
image classification; image colour analysis; image segmentation; support vector machines; RGB color attributes; color image segmentation; nonsupervised classification; support vector machines; Classification algorithms; Clustering algorithms; Color; Image segmentation; Object detection; Parametric statistics; Static VAr compensators; Support vector machine classification; Support vector machines; Systems engineering and theory; SVM; color attributes; image segmentation;
Conference_Titel :
Computational Engineering in Systems Applications, IMACS Multiconference on
Conference_Location :
Beijing
Print_ISBN :
7-302-13922-9
Electronic_ISBN :
7-900718-14-1
DOI :
10.1109/CESA.2006.4281718