# Review on Computer Aided Weld Defect Detection from Radiography Images
## 5 Defect Classification
### Feature Extraction  
The raw data of the defect image cannot be analyzed directly for further classification. Some measures or descriptors should be used to extract the characteristics of defects. This process is often called feature extraction. The classification method based on features of the defects is one of the most widely used techniques. The features with a lower dimension space than the raw data are usually human-defined according to the expertise or experience. The choice of proper features called feature engineering is a key factor for recognition of the defects in the intelligent system. This feature engineering is similar to the human interpretation, which recognizes a kind of weld defect according to visual information. At the beginning of the study, many scholars defined the features on a one-dimensional grayscale curve which are simple. Then more and more scholars began to focus on the shape, size, location, and the intensity of corresponding pixels of the defects in 2D images. Thus the geometric features and texture features were widely applied. The two kind of features defined by different scholars were also not identical. Furthermore, to synthesize the information obtained from the image, several scholars combined different kinds of features. After that, someone tried to apply features whose performance is good in other fields to weld detection. [PERSON] et al. processed each image line by line. For each line, they extracted 25 features, such as the degree of symmetricity (DOS), the median DOS and the goodness of fitting (GOF) etc. as the inputs of classifiers [30]. Furthermore, they extract three new features for each object in the line image for classification. These features are the width, the mean square error (MSE) between the object and its Gaussian, and the peak intensity (gray level) [31; 32]. [PERSON] calculated various parameters of profile plot as features for classification [33]. All the features mentioned above are based on the gray lever curve of line profile. The geometric features and texture features have been the most commonly used for weld defect classification in recent decades [6; 34]. The geometric features usually describe the shape, size, location, and intensity information of welding defects, while texture features can provide very useful visual cues commonly used in pattern recognition of image. [PERSON] extracted 12 numeric features from the segmented binary defect image, such as distance from center, radius mean, standard deviation, and circularity etc. [35]. Four geometric features including position, aspect ratio, ratio, roundness were extracted to build the inputs of nonlinear pattern classifier [36]. Three new geometric features were defined to be added the features for classification [37]. [PERSON] defined four new features: roughness of defect edge, roughness of defect region, skewness and kurtosis, which are closely related to the defect types but cannot be detected by human eyes [38]. The expert vision system proposed by [PERSON] was based on the features estimating the shape, orientation and location of the defect [39]. [PERSON] selected eight parameters regarding to weld center, symmetry, filling degree index and relative gray-scale as defect features, such as edge flatness and ratio of perimeter to area etc. [40]. [PERSON] extracted 28 texture features based on co-occurrence matrix for three distances, and 64 texture features based on Gabor functions for classification [41]. [PERSON] proposed the multimodal feature definition by combining the geometric features and texture features for capturing all visual attributes [29]. [PERSON] extracted a set of 8, 64 and 44 texture features vectors based on gray level co-occurrence matrix for classifying various defects [15]. Further, they combined the geometric features and texture features for classification, and compared the performance of classifiers with different feature sets.