# Review on Computer Aided Weld Defect Detection from Radiography Images
## 5 Defect Classification
### Classifier  
The choice of classifier is another key factor influencing the performance of defect classification. It is a more complex problem to choose an appropriate classifier. The classification task was finished by fuzzy K-NN algorithm and fuzzy c-means algorithm according to extracted features. It was concluded that the classification performance of fuzzy K-NN algorithm is superior to that of fuzzy c-means [30; 31]. [PERSON] and [PERSON] used three statistical classifiers which conducting the classification by using the concept of similarity: polynomial classifier, Mahalanobis classifier and nearest neighbor classifier [48]. They compared the performance of these classifiers with 7 selected features using the true positives, false positives, false negatives, true negatives, sensibility, and 1-specificity [41]. [PERSON] used fuzzy expert systems for classification and compared it with the fuzzy k-nearest neighbor algorithm and multi-layer perceptron neural networks. It is concluded that the proposed fuzzy system is more transparent and easier understood by human through the evaluation by bootstrap method [35]. [PERSON] found that multi-SVM is almost independent of the impact of the training samples reduction. Thus it had higher accuracy than fuzzy neural network under the condition of small samples [40]. [PERSON] used direct multiclass SVM (DMSVM) to classify the defects through the features defined by themselves. DMSVM yields a direct method for training multiclass predictors instead of constructing the classifier according to the samples to be classified [38]. [PERSON] implemented a study of nonlinear classifier using ANN and proved that the quality of the extracted features is more important than the quantity of the features [36]. In the last ten years, ANN has been widely used in the classification of welding defects [6; 12; 49]. [PERSON] applied ANN to classify the defect candidates. They used three different regularization methods (i.e., regularization with modified performance function, automatically setting of the regularization parameters and early stopping or bootstrap) to improve the network generalization. The network with best performance is obtained through several tests [49]. [PERSON] compared the performances of neural networks and decision trees. In this work, they pointed out the error rate cannot be the only criterion for comparing the different classifiers. More criteria, such as generalization, representation ability, classification quality and classification cost etc. were proposed [33]. They noted that the RBF neural net and the back-propagation network perform better than decision tree on the error rate for test data set while the unpruned decision tree shows the best error rate for the design data set. The representation and generalization ability of the neural networks is more balanced while the unpruned decision tree overfits the data. However, the neural nets need the feature selection before learning. Otherwise, the neural nets will be more complex and time-consuming. Moreover, the explanation capability of decision trees whose rules can be understood and controlled by human is strong. The selection of classifiers is a complex problem. Their accuracies are closely related not only to the extracted features, but also to the characteristics of the original data. For example, the complexity of some classifiers depends on the number of input features. Some classifiers need an additional feature selection operation when the number of the input features is large. Table 1 shows the primary coverage of most literatures for reader guidelines. The table shows the traditional computer vision technologies including the above-mentioned aspects. The results of various technologies show the only best accuracy or false alarm rate obtained by the classifiers with better performance. The results of different literature are generated through the different data using the different evaluation criteria. Thus it is not appropriate for the comparison.