Automatic Classification of Woven Fabric Structure Based on Texture Feature and PNN

In today's textile industry, the classification of woven fabrics is usually manual which requires considerable human efforts and a long time. With the rapid development of computer vision, the automatic and efficient methods for woven fabric classification are desperately needed. This paper proposes an automatic and real-time classification method to analyze three woven fabrics: plain, twill and satin weave. The methodology involves two approaches to extract texture features, that is, gray-level co-occurrence matrix (GLCM) and Gabor wavelet. Then, principal component analysis (PCA) is utilized to deal with the texture feature vectors to gain minimize redundancy and maximize principal component feature vectors. Finally, in the classification phase, probabilistic neural network (PNN) is applied to classify three basic woven fabrics. With strong real-time, fault-tolerance and non-linear classification capability, PNN can be a promising tool for classification of woven fabrics. The experimental results show that PNN classifier with faster training speed can classify woven fabrics accurately and efficiently. Besides, compared with GLCM method and Gabor wavelet method, the fusion of the two feature vectors obtains the best classification result (95 %).