Orthogonal locally discriminant spline embedding for plant leaf recognition

Abstract

Based on local spline embedding (LSE) and maximum margin criterion (MMC), two orthogonal locally discriminant spline embedding techniques (OLDSE-I and OLDSE-II) are proposed for plant leaf recognition in this paper. By OLDSE-I or OLDSE-II, the plant leaf images are mapped into a leaf subspace for analysis, which can detect the essential leaf manifold structure. Different from principal component analysis (PCA) and linear discriminant analysis (LDA) which can only deal with flat Euclidean structures of plant leaf space, OLDSE-I and OLDSE-II not only inherit the advantages of local spline embedding (LSE), but makes full use of class information to improve discriminant power by introducing translation and rescaling models. The proposed OLDSE-I and OLDSE-II methods are applied to recognize the plant leaf and are examined using the ICL-PlantLeaf and Swedish plant leaf image databases. The numerical results show compared with MMC, LDA, SLPP, and LDSE, the proposed OLDSE-I and OLDSE-II methods can achieve higher recognition rate.