Texture classification of fabric defects using machine learning

Yassine Ben Salem, Mohamed Naceur Abdelkrim


In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.


texture classification; image processing; woven fabric defects; GLCM; LBP; LVQ; SVM classifier

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DOI: http://doi.org/10.11591/ijece.v10i4.pp4390-4399

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578