Abstract Since the conventional shape descriptors focus on shape reexpression and the stochastic character of leaf shape is neglected, an effective plant leaf shape descriptor is proposed based on latent Dirichlet allocation (LDA) model. A corresponding leaf shape recognition framework is also constructed. Firstly, the plant leaf shape is transformed and represented as a multi-scale bag-of-words model, and thus the space interaction relationship is introduced into the leaf shape generative model. Furthermore, a leaf shape generative model is established via LDA model, and then the leaf shape descriptor is designed by the extracted shape distribution parameters in the LDA model. Finally, k-nearest neighbor (KNN) method is applied to the plant leaf shape classification. Experimental results demonstrate that the leaf shape descriptor combined with LDA model effectively improves the shape classification accuracy, especially for the plants of different classes but with a roughly similar shape of leaf. The proposed method obtains a higher classification accuracy compared with the conventional shape descriptor.
Fund:Supported by National Natural Science Foundation of China (No. 61571247), International S&T Cooperation Program of Ministry of Science and Technology of China (No.2013DFG12810), Natural Science Foundation of Zhejiang Province (No.LZ16F030001), International S&T Cooperation Program of Zhejiang Province (No.2013C24027), Graduate Science and Technology Innovation Project of Ningbo University (No.G14066)
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2016, Vol. 29 
