Texture image recognition based on modified probabilistic neural network

Dingqiang Yang; Shuping Xiao; Jiafu Jiang

doi:10.1117/12.747405

15 November 2007 Texture image recognition based on modified probabilistic neural network

Dingqiang Yang, Shuping Xiao, Jiafu Jiang

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880N (2007) https://doi.org/10.1117/12.747405
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Differential Evolution (DE) method is introduced in this paper to make up the insufficiency of basic probabilistic neural network. Consequently, a new texture image recognition method based on Modified Probabilistic Neural Network (MPNN) is proposed. At first, tree structure wavelet packet transformation is used to extract the energy characteristic, and statistical method is used to extract the statistical mean value, average energy, standard deviation, and mean residual characteristics for obtaining the feature vector; then the feature vector of texture image is trained by the MPNN, thus the texture image is identified. The experiment result indicates that, compared to the BP neural network, RBF neural network, and the basic probabilistic neural network, the modified probabilistic neural network has higher accuracy and faster convergence speed.

Citation Download Citation

Dingqiang Yang, Shuping Xiao, and Jiafu Jiang "Texture image recognition based on modified probabilistic neural network", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880N (15 November 2007); https://doi.org/10.1117/12.747405

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