Two-Dimensional Locality Discriminant Projection for Plant Leaf Classification
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.
KeywordsLocality Preserving Projections (LPP) Two-dimensionality Locality Discriminat Projections (2D-LDP) Plant leaf Classification
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