基于降维LBP与叶片形状特征的植物叶片识别方法

为解决由于植物叶片特征的相似性以及叶片旋转导致植物识别率较低的问题，提出一种基于降维局部二值模式（LBP）与叶片形状特征相结合的植物叶片识别方法。首先利用LBP算法提取高维叶片纹理信息，通过主成分分析方法（PCA）对高维叶片特征降维；同时考虑叶片的形状特征，将LBP旋转不变性特征与叶片形状特征有效结合，在低维空间利用k近邻法（KNN）实现叶片的分类与识别。实验结果表明该方法具有较好的识别效果。

Plant leaves recognition method based on dimension reduction local binary pattern and shape features of leaves

In order to resolve the problem that the shape similarities and rotation of plants leaves will lower the accuracy of plant recognition, a?method?of?recognizing?plants?leaves is proposed, which is based on the dimension reduction LBP algorithm and the shape features of leaves. Firstly, the LBP algorithm is used to extract high dimensional texture features of leaves. Then PCA is used to reduce the feature dimensions. At the same time, the shape features of the leaves are considered. The LBP rotation invariant features are combined with the shape features effectively. In the low dimensional space, the plant can be classified and recognized by using k Nearest Neighbor method（KNN）. The experimental findings prove that this method can accomplish the recognition effectively.