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Two-Dimensional Locality Discriminant Projection for Plant Leaf Classification

  • Shan-Wen Zhang
  • Chuan-Lei Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

Compared to vector based manifold learning methods, image based methods can reduce the complexity of algorithm, avoid the small sample size problem and give more spatial structural information of image. Two-dimensionality Locality Discriminat Projections (2D-LDP) is proposed, which an effective dimensionality reduction method and benefits from three parts, i.e., Locality Preserving Projections (LPP) algorithm, image based projection and discriminant analysis. In this paper, we apply 2DLPP to plant leaf classification. 2D-LDP can detect the intrinsic class-relationships between the leaf images by incorporating both class label information and neighborhood information. The Experimental results show that 2D-DLPP has better classifying performance than other methods.

Keywords

Locality Preserving Projections (LPP) Two-dimensionality Locality Discriminat Projections (2D-LDP) Plant leaf Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shan-Wen Zhang
    • 1
    • 2
  • Chuan-Lei Zhang
    • 3
  1. 1.Department of Engineering and TechnologyXijing UniversityXi’anChina
  2. 2.Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  3. 3.Department of Electrical and Computer EngineeringRyerson UniversityCanada

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