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基于多特征融合和深度信念网络的植物叶片识别

刘念 阚江明

刘念, 阚江明. 基于多特征融合和深度信念网络的植物叶片识别[J]. 北京林业大学学报, 2016, 38(3): 110-119. doi: 10.13332/j.1000-1522.20150267
引用本文: 刘念, 阚江明. 基于多特征融合和深度信念网络的植物叶片识别[J]. 北京林业大学学报, 2016, 38(3): 110-119. doi: 10.13332/j.1000-1522.20150267
LIU Nian, KAN Jiang-ming. Plant leaf identification based on the multi-feature fusion and deep belief networks method[J]. Journal of Beijing Forestry University, 2016, 38(3): 110-119. doi: 10.13332/j.1000-1522.20150267
Citation: LIU Nian, KAN Jiang-ming. Plant leaf identification based on the multi-feature fusion and deep belief networks method[J]. Journal of Beijing Forestry University, 2016, 38(3): 110-119. doi: 10.13332/j.1000-1522.20150267

基于多特征融合和深度信念网络的植物叶片识别

doi: 10.13332/j.1000-1522.20150267
基金项目: 

国家自然科学基金项目(30901164)

详细信息
    作者简介:

    刘念。主要研究方向:图像处理、模式识别。Email:bjfuln@163.com 地址:100083 北京市海淀区清华东路35号北京林业大学工学院。
    责任作者: 阚江明,博士,教授。主要研究方向:机器视觉、智能信息处理。Email: kanjm@bjfu.edu.cn 地址:同上。

    刘念。主要研究方向:图像处理、模式识别。Email:bjfuln@163.com 地址:100083 北京市海淀区清华东路35号北京林业大学工学院。
    责任作者: 阚江明,博士,教授。主要研究方向:机器视觉、智能信息处理。Email: kanjm@bjfu.edu.cn 地址:同上。

Plant leaf identification based on the multi-feature fusion and deep belief networks method

  • 摘要: 基于叶片数字图像的植物识别是自动植物分类研究的热点。但是随着植物种类的增加,传统的分类方法由于提取的特征比较单一或者分类器结构过于简单,导致叶片识别率较低。为此,本文提出使用纹理特征结合形状特征进行识别,并且使用深度信念网络构架作为分类器。纹理特征通过局部二值模式、Gabor滤波和灰度共生矩阵方法得到。而形状特征向量由Hu氏不变量和傅里叶描述子组成。为了避免过拟合现象,使用“dropout”方法训练深度信念网络。这种基于多特征融合的深度信念网络的植物识别方法,在Flavia数据库中,对32种叶片的识别率为99.37%;在ICL数据库中,对220种叶片的识别率为93.939%。这表明相比一般的叶片识别方法,此方法鲁棒性更强,并且识别率更高。

     

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出版历程
  • 收稿日期:  2015-07-21
  • 刊出日期:  2016-03-31

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