Plant species recognition based on global-local maximum margin discriminant projection

Abstract 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.