Paper Title:
Image Classification for Steel Strip Surface Defects Based on Support Vector Machines
  Abstract

In order to realize less time consuming and on-line image classification for steel strip surface defects, an improved multiclass support vector machine (SVM) was proposed. The SVM used a novel algorithm and only constructed (k-1) two-class SVMs where K is the number of classes. In the testing phase, to identify the surface defects it used a new unidirectional acyclic graph which had internal (k-1) nodes and k leaves. Its testing time is less than traditional multiclass SVM method. The experiment results shows that this method is simple and less time consuming while preserving generalization ability and recognition accuracy toward steel strip surface defects.

  Info
Periodical
Advanced Materials Research (Volumes 217-218)
Edited by
Zhou Mark
Pages
336-340
DOI
10.4028/www.scientific.net/AMR.217-218.336
Citation
Y. W. Yu, G. F. Yin, L. Q. Du, "Image Classification for Steel Strip Surface Defects Based on Support Vector Machines", Advanced Materials Research, Vols. 217-218, pp. 336-340, 2011
Online since
March 2011
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