Abstract
This paper proposed a new method of extracting texture features based on contourlet domain in RGB color space. In addition, the application of these features for bark classification applying radial basis probabilistic network (RBPNN) has been introduced. In this method, the bark texture feature is firstly extracted by decomposing an image into 6 subbands using the 7-9 biorthogonal Debauches wavelet transform, where each subband is fed to the directional filter banks stage with 32 directions at the finest level, then the mean and standard deviation of the image output are computed. The obtained feature vectors are fed up into RBPNN for classification. Experimental results show that, features extracted using the proposed approach can be more efficient for bark texture classification than gray bark image.
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References
David, A.C., Deng, H.: Design-Based Texture Feature Fusion Using Gabor Filters and Co-Occurrence Probabilities. IEEE Transactions on Image Processing 14(7), 925–936 (2005)
Chi, Z., Houqiang, L., Chao, W.: Plant Species Recognition Based on Bark Patterns Using Novel Gabor Filter Banks. IEEE Int. Conf. Neural Networks & Signal Processing Nanjing, 1035–1038 (2003)
Cunha, J.B.: Application of Image Processing Techniques in the Characterization of Plant Leafs. International Symposium on Industrial Electronics 1, 612–616 (2003)
Do, M.N., Vetterli, M.: Rotation Invariant Texture Characterization and Retrieval Using Steerable Wavelet-Domain Hidden Markov Models. IEEE Trans. Multimedia 4(4), 517–527 (2002)
Po, D.D.-Y.: Image Modeling in Contourlet Domain, Master’s thesis, University of Illinois at Urbana-Champaign (2003)
Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)
Do, M.N.: Contourlet Toolbox, http://www.ifp.uiuc.edu/minhdo/software/
Hwang, W.-J., Wen, K.-W.: Fast kNN Classification Algorithm Based on Partial Distance Search. Electronics Letters 34(21), 2062–2063 (1998)
Huang, D.S., Zhao, W.-B.: Determining the Centers of Radial Basis Probabilities Neural Networks by Recursive Orthogonal Least Square Algorithms. Applied Mathematics and Computation 162(1), 461–473 (2005)
Huang, D.S.: Application of Generalized Radial Basis Function Networks to Recognition of Radar Targets. International Journal of Pattern Recognition and Artificial Intelligence 13(6), 945–962 (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Huang, ZK., Quan, ZH., Du, JX. (2006). Bark Classification Based on Contourlet Filter Features Using RBPNN. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_138
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DOI: https://doi.org/10.1007/11816157_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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