Wavelet packet and generalized gaussian density based textile pattern classification using BP neural network

Authors:
Yean Yin;Liang Zhang;Miao Jin;Sunyi Xie
Affiliations:
College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China;College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China;College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China;College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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Abstract

This paper presents a combined approach to classify the textile patterns based on wavelet packet decomposition and a BP neural network classifier. On the accurate modeling of the marginal distribution of wavelet packet coefficients using generalized Gaussian density (GGD), two parameters are calculated for every level wavelet packet sub-band by moment matching estimation (MME) or by maximum likelihood estimation (MLE). The parameter vectors then are taken as the pattern matrix to a BP neural network for recognition. The proposed method was verified by experiments that using 16 classes of textile patterns, in which the correct recognition rate is as high as 95.3%.