Toward intelligent fashion: a fabric-aware apparel classification using lightweight machine learning models

Purpose This study aims to present an integrated fabric classification system aimed at enhancing personalized, season-aware fashion recommendations. The objective is to accurately identify suitable fabric types – such as cotton for summer and wool for winter – using lightweight machine learning techniques, thereby enabling intelligent outfit suggestions based on fabric properties and climatic relevance. Design/methodology/approach The proposed framework uses a hybrid feature extraction strategy that combines RGB and luminance-based statistical descriptors with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). Random Forest (RF)-based feature selection is used to select key features and reduce dimensionality. The optimized features are then classified using support vector machine (SVM) and K-Nearest Neighbors (KNN). The data set comprises iBUG images augmented with custom samples to ensure balanced representation of major fabric types – Velvet, Silk, Cotton and Denim. Findings Experimental evaluation reveals that the SVM classifier with a polynomial kernel achieves an accuracy of 97.8%, demonstrating the robustness of the proposed model. The KNN classifier, using the Chebyshev distance, strikes a favorable balance between precision and computational efficiency, making it suitable for real-time or resource-constrained applications. Practical implications The system offers a scalable solution for fashion retail, e-commerce and smart textiles. Linking fabric identification with seasonal preferences enhances recommendation accuracy, customer satisfaction and inventory management. Originality/value The novelty lies in the unified integration of color, texture and statistical features with machine learning methods, offering a precise and computationally efficient approach to fabric recognition.