Two-Dimensional locality discriminant projection for plant leaf classification

Authors:
Shan-Wen Zhang;Chuan-Lei Zhang
Affiliations:
Department of Engineering and Technology, Xijing University, Xi'an, China,Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China;Department of Electrical and Computer Engineering, Ryerson University, Canada
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

Citing 6
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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.