Title :
Supervised texture segmentation using support vector machines
Author :
Kim, K.I. ; Jung, K. ; Park, S.H. ; Kim, H.J.
Author_Institution :
Dept. of Comput. Eng., Kyungpook Nat. Univ., Taegu, South Korea
fDate :
10/28/1999 12:00:00 AM
Abstract :
An approach to the problem of supervised texture segmentation using nonlinear support vector machines (SVMs) is presented. For each texture class a nonlinear SVM is constructed which separates that class from the other classes. The segmentation then works by applying all the SVMs to an input image and arbitrating between the SVM outputs. Experimental results show the effectiveness of the proposed method
Keywords :
image segmentation; image texture; learning (artificial intelligence); neural nets; vector processor systems; input image; nonlinear support vector machines; supervised texture segmentation; texture class;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19991317
