Fabric Defect Identification based on KNN and PCA Algorithms
Subject Areas : Pattern Recognition
Zahra Nouri
1
,
Farahnaz Mohanna
2
*
,
Mina Boluki
3
1 - Department of Telecommunication Engineering, Electrical and Computer Engineering Faculty, University of Sistan and Baluchestan, Zahedan, Iran
2 - Dept. of Communication University of Sistan and Baluchestan Zahedan, Iran
3 - Dept. of Communication University of Sistan and Baluchestan Zahedan, Iran
Keywords: Fabric Defect Identification, Feature Extraction, KNN Classifier, PCA Algorithm,
Abstract :
In this study, the k-nearest neighbor classifier is used for fabric defect identification. First, the fabric image grey-level directional co-occurrence matrix (GLCM) is computed in two directions of , and . Next, 6 intensities features of the image are calculated from the GLCM. Second, the fabric image grey levels minimum, maximum, median, and mean are also computed. Third, these 16 features are gathered in a vector as the input image feature vector. Fourth, the principal component analysis algorithm is used to decrease the fabric image feature vector size. Fifth, the k-nearest neighbor classifier is used to cluster the reduced features. In this stage, the input image is classified as defect or free defect according to the training data. In following to locate the defects, defect patches are segmented from the fabric image with the defect. Then, the features of defective patches are calculated, decreased using the principal component analysis algorithm, and classified by the k-nearest neighbor classifier. Finally, each defect class is obtained and the defect positions are illustrated by morphology. The performance of the proposed method is measured in the TILDA database images. The results of evaluation show the mean average accuracy of 95.65% for fabric defect identification in the database.
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