Yaolin Zhu, Lu Zhao, Xin Chen, Yunhong Li, Jinmei Wang
{"title":"Animal fiber recognition based on feature fusion of the maximum inter-class variance","authors":"Yaolin Zhu, Lu Zhao, Xin Chen, Yunhong Li, Jinmei Wang","doi":"10.2478/aut-2022-0031","DOIUrl":null,"url":null,"abstract":"Abstract Cashmere and wool are common raw materials in the textile industry. The clothes made of cashmere are popular because of the excellent comfort. A system that can quickly and automatically classify the two will improve the efficiency of fiber recognition in the textile industry. We propose a classification method of cashmere and wool fibers based on feature fusion using the maximum inter-class variance. First, the fiber target area is obtained by the preprocessing algorithm. Second, the features of sub-images are extracted through the algorithm of the Discrete Wavelet Transform. It is linearly fused by introducing the weight in the approximate and detailed features. The maximum separability of the feature data can be achieved by the maximum inter-class variance. Finally, different classifiers are used to evaluate the performance of the proposed method. The support vector machine classifier can achieve the highest recognition rate, with an accuracy of 95.20%. The experimental results show that the recognition rate of the fused feature vectors is improved by 6.73% compared to the original feature vectors describing the image. It verifies that the proposed method provides an effective solution for the automatic recognition of cashmere and wool.","PeriodicalId":49104,"journal":{"name":"Autex Research Journal","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autex Research Journal","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.2478/aut-2022-0031","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, TEXTILES","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract Cashmere and wool are common raw materials in the textile industry. The clothes made of cashmere are popular because of the excellent comfort. A system that can quickly and automatically classify the two will improve the efficiency of fiber recognition in the textile industry. We propose a classification method of cashmere and wool fibers based on feature fusion using the maximum inter-class variance. First, the fiber target area is obtained by the preprocessing algorithm. Second, the features of sub-images are extracted through the algorithm of the Discrete Wavelet Transform. It is linearly fused by introducing the weight in the approximate and detailed features. The maximum separability of the feature data can be achieved by the maximum inter-class variance. Finally, different classifiers are used to evaluate the performance of the proposed method. The support vector machine classifier can achieve the highest recognition rate, with an accuracy of 95.20%. The experimental results show that the recognition rate of the fused feature vectors is improved by 6.73% compared to the original feature vectors describing the image. It verifies that the proposed method provides an effective solution for the automatic recognition of cashmere and wool.
期刊介绍:
Only few journals deal with textile research at an international and global level complying with the highest standards.
Autex Research Journal has the aim to play a leading role in distributing scientific and technological research results on textiles publishing original and innovative papers after peer reviewing, guaranteeing quality and excellence.
Everybody dedicated to textiles and textile related materials is invited to submit papers and to contribute to a positive and appealing image of this Journal.