Binary-tree Multi-Classifier for Welding Defects and Its Application Based on SVM

Application of support vector machine (SVM) for large number of catalogs was studied, and the structure of optional SVM decision tree was introduced under the different background. Aiming at the defect recognition problem in X-ray inspection welding images and combining the basic theory of binary-tree, a binary-tree method of multi-class classification based on SVM was put forward. This method adopts ´one-against-all´ classification algorithm by which binary-tree multi-classifier for welding defects based on SVM was established. According to the characteristics of defects, six parameters were chosen as feature parameters, and familiar defects in the weld were classified into 6 classes. 84 defect samples were used to experiments, and the results show that the classifier possesses simple, intuitionistic and practical algorithm, and small number of repeated training samples