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基于聚类分析和支持向量机的布匹瑕疵分类方法
引用本文:刘万春,罗双华,朱玉文,谢世斌.基于聚类分析和支持向量机的布匹瑕疵分类方法[J].北京理工大学学报,2004,24(8):687-690.
作者姓名:刘万春  罗双华  朱玉文  谢世斌
作者单位:北京理工大学,信息科学技术学院计算机科学工程系,北京,100081
基金项目:中国纺织品进出口总公司资助项目
摘    要:提出一种基于聚类分析和支持向量机(SVM)的布匹瑕疵分类方法.该方法充分利用瑕疵的几何特征,首先使用迭代自组织数据分析技术算法(ISODATA)对其进行聚类,在聚类形成的子空间内再根据瑕疵的纹理特征利用SVM进行分类.根据布匹瑕疵的特点提出一种新的几何特征,并使用各类瑕疵的几何特征均值作为初始聚类中心,提高ISODATA算法的聚类效果.实验表明,该方法有效地提高了分类准确性,降低了训练的复杂度,分类准确率可达90%.

关 键 词:瑕疵分类  聚类  支持向量机  特征提取  聚类分析  支持向量机  布匹瑕疵  分类方法  Support  Vector  Machine  Cluster  Analysis  Based  Classification  Defect  分类准确率  复杂度  训练  分类准确性  实验  聚类效果  初始聚类中心  均值  纹理特征  子空间  ISODATA
文章编号:1001-0645(2004)08-0687-04
收稿时间:2003/9/25 0:00:00
修稿时间:2003年9月12日

Fabric Defect Classification Based on Cluster Analysis and Support Vector Machine
LIU Wan-chun,LUO Shuang-hu,ZHU Yu-wen and XIE Shi-bin.Fabric Defect Classification Based on Cluster Analysis and Support Vector Machine[J].Journal of Beijing Institute of Technology(Natural Science Edition),2004,24(8):687-690.
Authors:LIU Wan-chun  LUO Shuang-hu  ZHU Yu-wen and XIE Shi-bin
Institution:Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China
Abstract:Presents an efficient method of fabric defect classification based on cluster analysis and support vector machine (SVM). The iterative self-organizing data analysis technique algorithm(ISODATA) is applied to cluster the defects,SVM is then used to classify each cluster. The paper presents a new geometric feature according to the characteristics of fabric defects, and use the mean geometric feature value of each defect class as the initial clustering center to improve the result of clustering. Experimental results show that the method improves the precision of classification effectively and reduces the complexity in training. The overall classification precision reaches 90%.
Keywords:defect classification  clustering  support vector machine  feature extraction
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