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Plant Leaf Recognition through Local Discriminative Tangent Space Alignment


Affiliations
1 School of Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
2 Department of Electronics and Information Engineering, Xijing University, Xi’an 710123, China
3 Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China
 

Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.
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  • Plant Leaf Recognition through Local Discriminative Tangent Space Alignment

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Authors

Chuanlei Zhang
School of Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
Shanwen Zhang
Department of Electronics and Information Engineering, Xijing University, Xi’an 710123, China
Weidong Fang
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

Abstract


Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.