基于叶片图像多特征融合的观叶植物种类识别

叶片图像特征提取对于植物自动分类识别有着重要的研究意义。本文以观叶植物叶片为研究对象,综合提取叶片图像的颜色、形状和纹理特征,基于支持向量机(SVM)原理提出了基于图像分析的观叶植物自动识别分类方法。通过对50种观叶植物样本图像进行训练和识别,与BP神经网络和KNN识别方法进行比较,本文所采用的SVM分类器的识别率能够达到91.41%,取得了较好的识别效果。 

Method of identification of foliage from plants based on extraction of multiple features of leaf images

The extraction of features of images of plant leaves is important for automatic classification and identification of plants. In this study, plant leaves were used as research objects. Through synthetic extraction of color, shape and texture features of leaf images, a method for automatical classification and identification of plants is proposed based on image analysis of a SVM (support vector machine) principle. After training and recognizing images of fifty parts of foliage of plants, our classifier has achieved good performance with a recognition rate of 91.41%,compared with the results of the BP (Back Propagation) neural network and the KNN (K-Nearest Neighbor) identification method.