Image recognition method of cashmere and wool based on SVM-RFE selection with three types of features

Abstract Cashmere and wool fibers are important raw materials in the textile industry, but their similar morphological structures make accurate distinctions challenging. Image preprocessing methods will cause some damage to the fiber contours, resulting in the loss of feature information. The keypoint features that do not require image preprocessing are added to the library of morphological and texture features. At the same time, existing methods of feature selection often ignore the relation between features and the classifier. Therefore, we propose a novel feature selection method with support vector machine-recursive feature elimination (SVM-RFE). The SVM-RFE method recursively removes the features of the least contribution to SVM classification, ultimately generating the optimal feature set. Our approach achieves a recognition accuracy of 98.06%, which is 8.34% higher than the traditional two-feature method and 6.12% higher than the three-feature method, both without feature selection. Experimental results demonstrate that keypoint features effectively compensate for the information loss caused by image preprocessing, while the SVM-RFE feature selection method can select the optimal feature subset relevant to the classifier so as to accurately distinguish cashmere and wool fibers.