Orthogonal locally discriminant spline embedding for plant leaf recognition

Authors:
Ying-Ke Lei;Ji-Wei Zou;Tianbao Dong;Zhu-Hong You;Yuan Yuan;Yihua Hu
Affiliations:
The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China and Electronic Engineering Institute, Hefei, Anhui 2300 ...;Electronic Engineering Institute, Hefei, Anhui 230027, China;Electronic Engineering Institute, Hefei, Anhui 230027, China;College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China;Electronic Engineering Institute, Hefei, Anhui 230027, China;Electronic Engineering Institute, Hefei, Anhui 230027, China
Venue:
Computer Vision and Image Understanding
Year:
2014

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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.