计算机应用 ›› 2013, Vol. 33 ›› Issue (07): 2009-2013.DOI: 10.11772/j.issn.1001-9081.2013.07.2009

• 多媒体技术 • 上一篇    下一篇

基于克隆选择算法和K近邻的植物叶片识别方法

张宁,刘文萍   

  1. 北京林业大学 信息学院,北京 100083
  • 收稿日期:2013-01-29 修回日期:2013-02-27 出版日期:2013-07-01 发布日期:2013-07-06
  • 通讯作者: 刘文萍
  • 作者简介:张宁(1985-),女,河北肃宁人,硕士研究生,主要研究方向:基于图像的植物叶片模式识别、数字图像处理;刘文萍(1971-),女,河北清苑人,教授,博士生导师,博士,主要研究方向:数字图像处理、视频分析与检索、模式识别、人工智能。
  • 基金资助:

    国家“973”计划项目

Plant leaf recognition method based on clonal selection algorithm and K nearest neighbor

ZHANG Ning,LIU Wenping   

  1. College of Information, Beijing Forestry University, Beijing 100083, China
  • Received:2013-01-29 Revised:2013-02-27 Online:2013-07-06 Published:2013-07-01
  • Contact: LIU Wenping
  • Supported by:

    National Key Basic Research and Development (973) Plan Projects

摘要: 针对植物叶片识别中分类器设计和训练识别时间较长的问题,提出了一种基于人工免疫系统下的克隆选择算法和K近邻判别分析(CSA+KNN)的叶片识别方法。进行图像预处理后,通过提取叶片的几何特征和纹理特征得到叶片综CSA+KNN进行植物叶片样本训练,并进行植物叶片识别。在100种植物叶片数据库中进行测试,CSA+KNN法识别率为91.37%。与BP神经网络等方法相比较,实验结果表明了该识别方法的有效性以及较高的训练速率,同时验证了纹理特征在叶片识别中的重要性。CSA+KNN法扩宽了植物叶片的识别方法,可应用于建立数字化植物标本博物馆等领域。

关键词: 植物叶片识别, 克隆选择算法, 人工免疫系统, 数字图像分析, 几何特征, 纹理特征

Abstract: To decrease the time of classifier design and training, a new method combining the Clonal Selection Algorithm and K Nearest Neighbor (CSA+KNN) was proposed. Having the image preprocessed and getting the comprehensive features information from geometry and texture feature, the CSA+KNN was used to train and classify the plant leaf samples. The plant leaf database with 100 leaf species was applied to test the proposed algorithm, and the recognition accuracy was 91.37%. Compared with other methods, the experimental results demonstrate the efficiency, accuracy and high training speed of the proposed method, and verify the significance of texture features in leaf recognition. CSA+KNN method broadens the field of plant leaf recognition method, and it can be applied to create digitalized plant specimens museum.

Key words: plant leaf recognition, Clonal Selection Algorithm (CSA), Artificial Immune System (AIS), digital image analysis, geometry feature, texture feature

中图分类号: