Multi feature fusion fiber classification algorithm based on support vector machine
A new multi feature fusion fiber classification algorithm suitable for multi type fiber image recognition and classification were proposed to address the problem that commonly used manual identification methods in the market cannot recognize and classify multiple types of fibers.Firstly,the grayscale histograms,local binary patterns(LBP),directional gradient histograms(HOG),Hu invariant moments,and gray level co-occurrence matrix(GLCM)features of 10 types of fiber images were extracted.Then,the above features were weighted and fused to obtain a new feature,which is trained(8 000 pices fiber)and tested(2 000 pices fiber)using an support vector machine(SVM)model to obtain the final recognition accuracy.The experimental results show that the average accuracy of the algorithm is 85.8%,among which the characteristics of acrylic fiber,acetate fiber,and nylon fiber are very obvious,and the accuracy reaches over 90%.At the same time,the accuracy of difficult to distinguish wool and cashmere fibers also reaches about 88%.This algorithm has achieved good recognition results,providing a technical foundation for fast and accurate identification of fibers.
NETL
NSTL
万方数据
