Fabric classification based on recognition using a neural network and dimensionality reduction

Fabric classification plays an important role in the textile industry. In this paper, two fabric classification methods, the neural network and dimensionality reduction, are proposed to automatically classify fabrics based on measured hand properties. The methods are independent and reinforce each other. The first method adopts a neural network to recognize the category of an unknown fabric. In the second method, a dimensionality reduction technique is applied to reduce the dimensionality of the measured properties of input fabrics from sixteen dimensions to two. The reduced features are then plotted in a two-dimensional coordinate system to visualize and verify the classification results of the neural network. In experiments conducted to verify the validity of our proposed approach, fabric data are expressed in the form of hand properties extracted from the KES-FB system (Kawabata's evaluation system for fabrics). These experiments confirm the feasibility and efficiency of our approach with a wide variety of fabrics.