Fabric Defect Classification Based on Cluster Analysis and Support Vector Machine

Presents an efficient method of fabric defect classification based on cluster analysis and support vector machine (SVM). The iterative self-organizing data analysis technique algorithm(ISODATA) is applied to cluster the defects,SVM is then used to classify each cluster. The paper presents a new geometric feature according to the characteristics of fabric defects, and use the mean geometric feature value of each defect class as the initial clustering center to improve the result of clustering. Experimental results show that the method improves the precision of classification effectively and reduces the complexity in training. The overall classification precision reaches 90%.