Image classification method of cashmere and wool based on the multi-feature selection and random forest method

• Published in: Textile research journal Vol. 92; no. 7-8; pp. 1012 - 1025
• Main Authors: Zhu, Yaolin, Duan, Jiameng, Li, Yunhong, Wu, Tong
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.04.2022; Sage Publications Ltd
• Subjects: Accuracy; Algorithms; Cashmere; Classification; Correlation analysis; Datasets; Feature extraction; Feature selection; Image classification; Principal components analysis; Textile industry; Wool; Cashmere and wool classification; correlation analysis; multi-feature selection; random forest; principal component analysis
• ISSN: 0040-5175; 1746-7748
• DOI: 10.1177/00405175211046060

Cashmere and wool play an important role in the wool industry and textile industry, and suitable features are the key to identifying them. To obtain effective features and improve the accuracy of cashmere and wool classification, the multi-feature selection and random forest method is used to express in this article. Firstly, the gray-gradient co-occurrence matrix model is used for texture feature extraction to construct the original high-dimensional feature data set; secondly, considering that the original feature data set contains a large number of invalid and redundant features, the feature selection algorithm combining correlation analysis and principal component analysis–weight coefficient evaluation is used to obtain important features, independent features, and principal component sensitive features to complement each other; last but not least, the optimized random forest model analyzes the results. The results show that the combination of multi-feature selection subsets and random forest makes the classification accuracy of cashmere and wool more reliable, and the accuracy fluctuates around 90%.