Fabric defect detection based on feature-selected redundant GLCM

Abstract

Gray-level co-occurrence matrix features are widely applicable to texture analysis and texture-related tasks, but their discriminative power is reduced to varying degrees in complex backgrounds. To overcome this limitation, we propose a Redundant GLCM (RGLCM) framework that leverages multi-scale image decomposition and a hybrid feature extraction scheme. The input image is first processed using multiple Generalized Gaussian Filters with distinct shape parameters (β) to generate low-frequency structural components, from which corresponding high-frequency difference maps are computed. Rather than symmetrically extracting high-overhead descriptors across all layers, the framework applies a structured mechanism: five rotation-invariant GLCM features are systematically extracted from both the n low-frequency filtered components and n high-frequency difference maps to isolate localized anomalies and background regularities simultaneously, yielding a comprehensive \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2n \times 5$$\end{document} dimensional representation. A data-driven selection strategy based on feature importance extracted with Random Forest is then employed to eliminate redundancy and identify the most discriminative descriptors. Experimental evaluation on the AITEX textile defect dataset demonstrates that the proposed RGLCM methodology significantly outperforms conventional single-scale methods in both detection accuracy and robustness while maintaining a highly compact feature representation. This method provides a structurally rigorous and computationally efficient framework for fabric detection, which can accurately capture microscopic anomalies and global texture patterns, and reduce feature redundancy.