Fabric Defect Classification Using Minimum-classification-error Based Wavelet Features
基于最小分类误差小波特征的纺织品缺陷分类方法研究

Fabric defect classification plays an important role in computer visionbased fabric quality inspection. In this paper, a novel defect classification method based on wavelet frames is proposed. Defects of texture properties are characterized using the wavelet frames. Minimum classification error training method is used to incorporate the design of a linear transform matrixbased feature extractor and a classifier, which yields classification-oriented wavelet features and minimizes the error rate associate with the classifier. The proposed method has been evaluated on the classification of 329 defect samples containing nine classes of fabric defects, and 328 non-defect samples. A 93.1% classification accuracy has been achieved which is 27.1% better than the traditional wavelet-based classification method.