International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 18, No. 1, 8 Feb. 2026
Cover page and Table of Contents: PDF (size: 1281KB)
A Novel Hybrid Approach Using MRMR-based Feature Selection and Bayesian Optimized Random Forest Classification for Accurate Fabric Defect Detection
PDF (1281KB), PP.128-149
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Author(s)
Ritu Juneja
1,*
Anil Dudy
1
Index Terms
Bayesian Optimization, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients, Local Binary Patterns, Minimum Redundancy Maximum Relevance, Random Forest Classifier
Abstract
The textile industry holds a central position in India's economy, contributing substantially to both employment and GDP. Despite technological advancements, maintaining stringent quality standards remains a persistent challenge due to defects such as cracks, stains, and inconsistencies in fabrics. Traditional manual inspection methods, while effective to a degree, are labor-intensive, time-consuming, and prone to human error. This paper proposes an innovative approach to address these challenges through the application of machine learning and computer vision techniques in fabric defect detection. Specifically, the research focuses on integrating advanced texture feature extraction methods—Gray-Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), and Histogram of Oriented Gradients (HOG)—with a robust classification framework using Bayesian optimized Random Forest. The methodology emphasizes efficient feature selection via Minimum Redundancy Maximum Relevance (MRMR), enhancing the system's accuracy and efficiency. By leveraging a comprehensive dataset from Kaggle encompassing diverse fabric defects, the proposed system aims to significantly improve defect detection accuracy, reduce manual intervention, and ensure consistent product quality across textile manufacturing processes. The highest accuracy achieved in the evaluation is 99.52%.
Cite This Paper
Ritu Juneja, Anil Dudy, "A Novel Hybrid Approach Using MRMR-based Feature Selection and Bayesian Optimized Random Forest Classification for Accurate Fabric Defect Detection", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.18, No.1, pp. 128-149, 2026. DOI:10.5815/ijigsp.2026.01.08
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