Fabric image classification algorithm based on improved 3E-LDA
Textiles are one of the three physical elements of clothing.In recent years,with the development of computer technology,the classification and recognition of fabric images and intelligent manufacturing have played a very important role in the textile field.In the process of production,traditional manual detection methods are still widely used for fabric defect detection,which is time-consuming and laborious,and the efficiency is very low.It is easy to cause false detection and missed detection due to fatigue,and it will also affect the quality and price of textiles.Therefore,the use of digital image processing technology to complete the recognition and classification of fabric images has become a hot issue in recent years.Relying on machine vision,spots,pits,scratches,color differences and defects on the fabric surface can be detected.Lineardiscriminant analysis(LDA)is a supervised dimensionality reduction and classification algorithm that can effectively classify fabric images.LDA classifies by calculating the optimal discriminant matrix to minimize the intra-class distance and maximize the inter-class distance.However,LDA is sensitive to outliers,ignores local geometric information and small sample size(SSS),which affects the classification accuracy.The 3E-LDA(three enhancements to linear discriminant analysis)algorithm improves the above three problems on the basis of LDA and improves the classification accuracy.However,when the number of training samples is smaller than the data dimension,it will reduce the model's resolution ability and ultimately affect the classification accuracy.A fabric image classification algorithm based on improved 3E-LDA,called I3E-LDA algorithm(Improved 3E-LDA),was proposed to address the problem of reduced model resolution caused by training samples being smaller than the data dimension.Firstly,the nonparametric weighted feature extraction(NWFE)method was used to regularize the intra-class scatter matrix,and then the goal combination method was used to introduce equilibrium parameters to regularize the objective function,so as to weaken the influence of outliers and noise,retain more discriminative feature data,and rely on these feature data to better classify fabric images.It is necessary to combine the improved null space learning method to solve the singularity and small sample problems of intra-class scatter matrices and improve classification efficiency,and to train and test on the Alibaba Tianchi fabric dataset and pattern fabric images to distinguish between normal and abnormal patterns(defect images).The experimental results show that the I3E-LDA algorithm effectively achieves fabric image classification,and improves classification accuracy for a small number of training samples(20%-40%of the samples are used for training).
linear discriminant analysisfabricimage classificationregularizationsmall sample size
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