Abstract
This paper proposed a new method of extracting texture features based on Gabor wavelet. 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 filtering the image with different orientations and scales filters, then the mean and standard deviation of the image output are computed, the image which have been filtered in the frequency domain. Finally, the obtained Gabor feature vectors are fed up into RBPNN for classification. Experimental results show that, first, features extracted using the proposed approach can be used for bark texture classification. Second, compared with radial basis function neural network (RBFNN), the RBPNN achieves higher recognition rate and better classification efficiency when the feature vectors have low-dimensions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
David, A.C., Huang, D.: Design-Based Texture Feature Fusion Using Gabor Filters and Co-Occurrence Probabilities. IEEE Transactions on Image Processing 14(7), 925–936 (2005)
Chi, Z., Li, H.Q., Wang, C.: Plant Species Recognition Based on Bark Patterns Using Novel Gabor Filter Banks. In: IEEE Int. Conf. Neural Networks & Signal Processing Nanjing, China, pp. 1035–1038 (2003)
Wan, Y.Y., Du, J.X., Huang, D.S., Chi, Z.R.: Bark Texture Feature Extraction Based on Statistical Texture Analysis. In: International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, pp. 482–185 (2004)
Manjunath, B.S., Ma, W.Y.: Texture features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI - Special issue on Digital Libraries) 18(8), 837–842 (1996)
Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)
Gamm, J.B., Yu, D.L.: Selecting Radial Basis Function Network Centers with Recursive Orthogonal Least Squares Training. IEEE Trans. Neural Network 11(2) (March 2000)
Zhao, W.B., Huang, D.S.: Application of Recursive Orthogonal Least Squares Algorithm to the Structure Optimization of Radial Basis Probabilistic Neural Networks. In: The 6th International Conference on Signal Processing (ICSP 2002), Beijing, China, pp. 1211–1214 (2002)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, ZK., Huang, DS., Du, JX., Quan, ZH., Guo, SB. (2006). Bark Classification Based on Gabor Filter Features Using RBPNN Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_9
Download citation
DOI: https://doi.org/10.1007/11893257_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)