Shape recognition based on radial basis probabilistic neural network and application to plant species identification

Authors:
Jixiang Du;Deshuang Huang;Xiaofeng Wang;Xiao Gu
Affiliations:
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China and Department of Automation, University of Science and Technology of China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

In this paper, a novel shape recognition method based on radial basis probabilistic neural network (RBPNN) is proposed. The orthogonal least square algorithm (OLSA) is used to train the RBPNN and the recursive OLSA is adopted to optimize the structure of the RBPNN. A leaf image database is used to test the proposed method. And a modified Fourier method is applied to descript the shape of the plant leaf. The experimental result shows that the RBPNN achieves higher recognition rate and better classification efficiency with respect to radial basis function neural network (RBFNN), BP neural network (BPNN) and multi-Layer perceptron network (MLPN) for the plant species identification.