Advertisement

Bark Classification Based on Gabor Filter Features Using RBPNN Neural Network

  • Zhi-Kai Huang
  • De-Shuang Huang
  • Ji-Xiang Du
  • Zhong-Hua Quan
  • Shen-Bo Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

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.

Keywords

Feature Vector Hide Layer Radial Basis Function Neural Network Gabor Filter Probabilistic Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhi-Kai Huang
    • 1
    • 2
  • De-Shuang Huang
    • 1
  • Ji-Xiang Du
    • 1
    • 2
  • Zhong-Hua Quan
    • 1
    • 2
  • Shen-Bo Guo
    • 1
    • 2
  1. 1.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefei, AnhuiChina
  2. 2.Department of AutomationUniversity of Science and Technology of China 

Personalised recommendations