基于克隆选择算法和K近邻的植物叶片识别方法  

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

作　　者：张宁[1] 刘文萍[1] 

机构地区：[1]北京林业大学信息学院,北京100083

出　　处：《计算机应用》2013年第7期2009-2013,共5页journal of Computer Applications

基　　金：国家973计划项目(2009CB421105)

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。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.

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 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]