基于特征选择与聚类优化的羊绒羊毛分类方法

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关键词细粒度纤维图像分类;类内马氏距离；类间欧式距离；特征选择;改进K-means

中图分类号：TP391.41文献标志码：A

AbstractTo address the challenge of low inter-class variance and high intra-class variance in cashmere and wool fiber image clasification，this paper proposed a fine-grained classification method that integrates feature selection and clustering optimization.First，morphological，textural，and keypoint features of cashmereand wool fibers were extracted to capture subtle diferences among fiber types.Then，a feature selection method based on intra-class and inter-class distance was employed to identify highly discriminative features and reduce redundant information.Finally，an improved K-means clustering algorithm was introduced，incorporating Mahalanobis distance forintra-class similarity and Euclidean distance for inter-class separation to identify and mitigate the influence of outliers.This enhances intra-cluster compactness and inter-cluster separability. The experimental results demonstrate that the proposed method achieves a classification accuracy of 98.92% on the cashmere/wool fiber image dataset，representing a 3.04% improvement over the baseline model.Simultaneously，the intra-class dispersion decreases by approximately 37% 0- verall，while the inter-class separation increases by about 26% ，indicating that the proposed method exhibits significant advantages in fine-grained fiber classification.

Keywordsfine-grained fiber image classification；intra-class mahalanobis distance；inter-class euclidean distance；feature selection；improved K-means

0 引言

羊绒和羊毛是重要的天然纤维原料，羊绒取自山羊腹部和颈部细绒，纤细柔软、保暖性好[2-3]，常用于高端服装产品和奢侈家纺用品。（剩余15297字）