06105338

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06105338 Zhang, Shan-Wen Zhang, Chuan-Lei Two-dimensional locality discriminant projection for plant leaf classification. Huang, De-Shuang (ed.) et al., Intelligent computing theories and applications. 8th international conference, ICIC 2012, Huangshan, China, July 25--29, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-31575-6/pbk). Lecture Notes in Computer Science 7390. Lecture Notes in Artificial Intelligence, 82-88 (2012). 2012 Berlin: Springer EN Locality Preserving Projections (LPP) Two-dimensionality Locality Discriminat Projections (2D-LDP) Plant leaf Classification

doi:10.1007/978-3-642-31576-3_11

Summary: 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.