摘要
提出一种基于Fisher投影的监督LLE方法,应用于植物叶片图像识别中。该方法利用Fisher投影距离取代样本的测地距离,并以此为基础计算样本的权值,加入LLE算法的代价函数中。该方法克服了传统LLE算法无监督学习不适应分类问题的缺陷,在抑制噪声点影响的同时可以更好地挖掘样本的类别信息,提高叶片的分类精度。基于实拍植物叶片图像数据库的实验结果证明,该算法的平均识别率达到92.36%。
A new supervised weighted LLE method based on the Fisher projection was proposed. This method utilized the Fisher projection distance to replace the sample＇s geodesic distance, and the importance score of each sample was obtained based on this distance, then the importance scores were added into the cost function of LLE. This method can overcome the disadvantage of traditional LLE, an unsupervised learning algorithm which cannot solve the classification problem very well, and can exploit the category information better and reduce the influence of noise points at the same time. The experimental results based on the real-world plant leaf databases show its mean accuracy of recognition is up to 92.36%.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2012年第9期179-183,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(61172127)
高等学校博士学科点专项科研基金资助项目(20113401120006)
安徽大学211创新团队项目(KJTD007A)
安徽大学"211工程"青年科学研究基金资助项目(KJQN1114)
关键词
植物叶片
识别
特征提取
监督局部线性嵌入
流形学习
FISHER变换
Plant leaves, Recognition, Feature extraction, Supervised locally linear embedding, Manifold learning, Fisher projection
