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Proceedings Paper

Texture image recognition based on modified probabilistic neural network
Author(s): Dingqiang Yang; Shuping Xiao; Jiafu Jiang
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Paper 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.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880N (15 November 2007); doi: 10.1117/12.747405
Show Author Affiliations
Dingqiang Yang, Changsha Univ. of Science and Technology (China)
Shuping Xiao, Changsha Univ. of Science and Technology (China)
Jiafu Jiang, Changsha Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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