摘要
对植物的分类多通过对植物叶片的分类来实现,为提高植物叶片分类的准确率提出了一种基于多特征融合与极限学习机的植物叶片分类方法。首先对植物叶片彩色图像进行预处理,得到去除叶片颜色与背景的二值图像和灰度图像;然后从二值图像中提取植物叶片的形状特征和不变矩特征,利用灰度图像提取灰度共生矩阵参数作为叶片图像的纹理特征,共得到28维的特征向量,最后采用极限学习机分类策略对特征向量进行训练和测试。在公开的植物叶片数据集Flavia上进行实验,训练分类准确率达到99%以上,测试准确率达到98%以上。实验结果表明,本文方法可以有效提高植物叶片分类的准确率。
The classification of plants is mostly realized through the classification of plant leaves.In order to improve the accuracy of plant leaf classification,a plant leaf classification method based on multi feature fusion and extreme learning machine is proposed.Firstly,the color image of plant leaves is preprocessed to get the binary image and gray image in order to remove the color and background of leaves.Secondly,the shape feature and invariant moment feature of plant leaves are extracted from the binary image,and the gray level co-occurrence matrix parameter is extracted from the gray level image as the texture feature of leaves,so a total of 28 dimensional feature vectors are obtained.Finally,the classification strategy of the extreme learning machine is used to train and test the eigenvectors.Experiments on the open plant leaf dataset Flavia show that the accuracy of training classification is more than 99%,and the test accuracy is more than 98%.Experimental results show that this method can effectively improve the accuracy of plant leaf classification.
作者
火元莲
李俞利
HUO Yuan-lian;LI Yu-li(College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China)
出处
《计算机工程与科学》
CSCD
北大核心
2021年第3期486-493,共8页
Computer Engineering & Science
基金
国家自然科学基金(61561044)。
关键词
植物叶片分类
多特征融合
极限学习机
plant leaf classification
multi-feature fusion
extreme learning machine
