Plant species recognition using leaves is an important and challenging research topic, because the plant leaves are various and irregular and they have very large within-class difference and between-class similarity. Considering that leaves have different discriminant performance and contribution to plant recognition task, based on maximum neighborhood margin discriminant projection (MNMDP), we propose a global–local maximum margin discriminant projection (GLMMDP) algorithm for plant recognition. GLMMDP utilizes the local and class information and the global structure of the data to model the intra-class and inter-class neighborhood scatters and a global scatter, obtaining the projection matrix by minimizing the local intra-class scatter and meanwhile maximizing both the local inter-class scatter and the global between-class scatter. Compared with MNMDP, GLMMDP not only can detect the true intrinsic manifold structure of the data, but also can enhance the pattern discrimination between different classes by incorporating the global between-class scatter into MNMDP. The global between-class scatter fully indicates the difference and similarity between classes. The experimental results on the ICL (Intelligent Computing Laboratory) leaf datasets and Leafsnap leaf image datasets demonstrate the effectiveness of the proposed plant recognition method. The recognition accuracy is more than 95% on the ICL datasets and more than 90% on Leafsnap datasets.

使用植物叶识别植物物种是一个重要且具有挑战性的研究课题，因为植物叶是多种多样且不规则的，并且具有很大的类内差异和类间相似性。考虑到叶子具有不同的判别性能和对植物识别任务的贡献，基于最大邻域边际判别投影（MNMDP），我们提出了一种全局-局部最大边际判别投影（GLMMDP）算法用于植物识别。GLMMDP利用本地和类信息以及数据的全局结构来建模类内和类间邻域散布以及全局散布，通过最小化局部类内散布并同时最大化局部类间散布来获得投影矩阵。类散布和全局类间散布。与MNMDP相比，GLMMDP不仅可以检测数据的真实内在流形结构，而且可以通过将全局类间散点合并到MNMDP中来增强不同类之间的模式辨别力。全局的类间散布完全表明了类之间的差异和相似性。在ICL（智能计算实验室）叶片数据集和Leafsnap叶片图像数据集上的实验结果证明了所提出的植物识别方法的有效性。在ICL数据集上，识别精度超过95％，在Leafsnap数据集上，识别精度超过90％。全局的类间散布充分表明了类之间的差异和相似性。在ICL（智能计算实验室）叶片数据集和Leafsnap叶片图像数据集上的实验结果证明了所提出的植物识别方法的有效性。在ICL数据集上，识别精度超过95％，在Leafsnap数据集上，识别精度超过90％。全局的类间散布完全表明了类之间的差异和相似性。在ICL（智能计算实验室）叶片数据集和Leafsnap叶片图像数据集上的实验结果证明了所提出的植物识别方法的有效性。在ICL数据集上，识别精度超过95％，在Leafsnap数据集上，识别精度超过90％。