摘要
针对纺织行业花式色纱纬编针织面料依据图案纹理自动分类问题,提出一种基于局部二值模式(Local binary pattern,LBP)和支持向量机(Support vector machine,SVM)的组合模型分类方法,对面料纹理进行特征提取和类别判定。对图片采用中值滤波去噪,通过优选采样模板半径和核函数,以LBP旋转不变模式提取面料纹理LBP特征直方图,并利用参数优化的SVM支持向量机的分类模型对面料进行分类。实验结果表明:采样模板半径为5像素点的LBP旋转不变式算法能较优地满足3类面料纹理的特征提取要求,经过参数优化的SVM分类模型,实现3类面料纹理分类的准确率达96.67%,分类效果较好。
To solve the problem of automatic classification of fancy color yarn weft knitted fabric according to pattern and texture in textile industry,a combined model classification method based on Local binary pattern(LBP)and Support Vector Machine(SVM)was proposed.Feature extraction and classification determination of fabric texture were carried out.The image was denoised by median filtering,and the LBP feature histogram of fabric texture was extracted by LBP rotation invariant mode by optimizing sampling template radius and kernel function,and the three kinds of fabric were classified by SVM classification model with optimized parameters.The experimental results show that the LBP rotation invariant algorithm with a radius of 5 pixels of sampling template can better meet the feature extraction requirements of the three kinds of fabric texture.The classification accuracy of the three kinds of fabric texture is 96.67%after the SVM classification model with optimized parameters,and the classification effect is good.
出处
《今日自动化》
2021年第11期127-129,共3页
Automation Today
关键词
花式色纱面料纹理
图像处理
特征提取
自动分类
fancy color yarn fabric texture
image processing
feature extraction
automatic classification
