Using Two Stage Classification for Improved Tropical Wood Species Recognition System

  • Anis Salwa Mohd Khairuddin
  • Marzuki Khalid
  • Rubiyah Yusof
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 11)


An automated wood recognition system is designed based on five stages: data acquisition, pre-processing images, feature extraction, pre classification and classification. The proposed system is able to identify 52 types of wood species based on wood features extracted using Basic Grey Level Aura Matrix (BGLAM) technique and statistical properties of pores distribution (SPPD) technique. The features obtained from both feature extractors are fused together and will determine the classification between the various wood species. In order to enhance the class separability, a pre-classification stage is developed which includes clustering and dimension reduction. K-means clustering is introduced to cluster the 52 wood species. As for dimension reduction, we proposed linear discriminant analysis (LDA) to solve linear data and kernel discriminant analysis/ generalized singular value decomposition (KDA/GSVD) to solve nonlinearly structured data. For final classification, K-Nearest Neighbour (KNN) classifier is implemented to classify the wood species.


wood recognition LDA KDA/GSVD K-means cluster kNN classifier 


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  1. 1.
    XiaoKai, Z., Xiang, L.: Image kernel for recognition. In: Proceedings of ICSP 2008. IEEE, Los Alamitos (2008)Google Scholar
  2. 2.
    Ye, F., Shi, Z., Shi, Z.: A comparative study of PCA, LDA and Kernel LDA for image classification. In: 2009 International Symposium on ubiquitous virtual reality. IEEE, Los Alamitos (2009)Google Scholar
  3. 3.
    Chen, W.-S., Yuen, P.C., Ji, Z.: Kernel subspace LDA with convolution kernel function for face recognition. In: 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao (2010)Google Scholar
  4. 4.
    Jiang, Y., Chen, X., Guo, P., Lu, H.: An improved random sampling LDA for face recognition. In: 2008 Congress on Image and Signal Processing (2008)Google Scholar
  5. 5.
    Park, C.H., Park, H.: Nonlinear discriminant analysis using kernel functions and the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications (2005)Google Scholar
  6. 6.
    Howland, P., Park, H.: Generalizing Discriminant Analysis Using the Generalized Singular Value Decomposition. IEEE Transaction, PAM (2004)Google Scholar
  7. 7.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Regularized discriminant analysis for the small sample size problem in face recognition. Pattern recognition Letters (2003)Google Scholar
  8. 8.
    Wang, L., Bo, L., Jiao, L.: Kernel uncorrelated discriminant analysis for radar target recognition. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Teknomo, Kardi. Discriminant Analysis Tutorial,
  10. 10.
    Qin, X., Yang, Y.-H.: Similarity measure and learning with gray level aura matrices (GLAM)for texture image retrieval. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2004, vol. 1, pp. I-326 - I-333 (2004)Google Scholar
  11. 11.
    Khalid, M., Lew, Y.L., Yusof, R., Nadaraj, M.: Design of An Intelligent Wood Species Recognition System. IJSSST 9(3) (September 2008)Google Scholar
  12. 12.
    Yusof, R., Rosli, N.R., Khalid, M.: Using Gabor Filters as Image Multiplier for Tropical Wood Species Recognition System. In: 12th International Conference on Computer Modelling and Simulation, pp. 289–294 (2010)Google Scholar
  13. 13.
    Wang, K., Bai, X.: Research on classification of wood surface texture based on feature level data fusion. In: 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA (2007)Google Scholar
  14. 14.
    Khairuddin, U., Yusof, R., Khalid, M., Cordova, F.: Optimized Feature Selection for Improved Tropical Wood Species Recognition System. ICIC Express letters, Part B: Applications, An International Journal of Research and Surveys 2(2), 441–446 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anis Salwa Mohd Khairuddin
    • 2
  • Marzuki Khalid
    • 1
  • Rubiyah Yusof
    • 1
  1. 1.Center for Artificial Intelligence and RoboticsUniversiti Teknologi MalaysiaKuala LumpurMalaysia
  2. 2.Department of Electrical EngineeringUniversity of MalayaKuala LumpurMalaysia

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