Automatic Classification of Woven Fabrics using Multi-class Support Vector Machine

Abstract

This paper proposes the recognition and classification of three mean woven fabrics, twill, satin and plain. The proposed classifier is based on the texture analysis of woven fabric images for the recognition.

In the pattern recognition phase, three methods are tested and compared: Gabor wavelet, Local Binary Pattern operators (LBP) and gray-level co-occurrence matrices (GLCM).

Taking advantage of the difference between the woven fabric textures, we adopt a technique which is based on the texture of the images in the pattern recognition phase. For the classification phase we used a support vector machine (SVM) which we have proven is a suitable classifier for this type of problem

The experimental results show that some of the studied methods are more compatible with this classification problem than others. Although it is the oldest method, GLCM always remains accurate (97.2 %).The fusion of the Gabor wavelet and GLCM give the best result (98%), but the GLCM have the better running time.

Citation

Ben Salem, Y. and Nasri, S. (2009), "Automatic Classification of Woven Fabrics using Multi-class Support Vector Machine", Research Journal of Textile and Apparel, Vol. 13 No. 2, pp. 28-36. https://doi.org/10.1108/RJTA-13-02-2009-B004