مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 13 52 2026 2 3 Fabric Defect Identification based on KNN and PCA Algorithms Fabric Defect Identification based on KNN and PCA Algorithms 326 332 10.66224/jist.49914.13.52.326 en Zahra Nouri University of Sistan and Baluchestan Farahnaz Mohanna University of Sistan and Baluchestan Mina Boluki University of Sistan and Baluchestan 2025 4 12 <p>In this study, a K-Nearest Neighbor (KNN) classifier is employed for fabric defect identification. First, directional Grey-Level Co-occurrence Matrix (GLCM) of the fabric image is computed in , and directions. Six intensity-based features are then extracted from these directional GLCMs. In addition, the minimum, maximum, median, and mean grey levels of the fabric image are computed. These sixteen features are combined into a single feature vector representing the fabric image. Next, Principal Component Analysis (PCA) is applied to reduce the dimensionality of the feature vector. The reduced features are then classified using the KNN classifier, categorizing each fabric image as either defective or defect-free based on training data. To localize defects, patches containing defects are segmented from the original fabric image. Features of these defect patches are extracted, reduced via PCA, and classified using KNN. Finally, each defect class is identified, and defect locations are visualized using morphological operations. The proposed method is evaluated on the comprehensive TILDA dataset, which contains 3,200) fabric images (both defective and defect-free). Experimental results demonstrate a mean average accuracy of 95.65% for fabric defect identification across classes , , and .&nbsp; &nbsp;</p> http://jist.ir/en/Article/Download/49914