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Machine Vision and Applications

, Volume 24, Issue 8, pp 1589–1604 | Cite as

Fuzzy logic-based pre-classifier for tropical wood species recognition system

  • Rubiyah YusofEmail author
  • Marzuki Khalid
  • Anis Salwa Mohd Khairuddin
Original Paper

Abstract

Classifying tropical wood species poses a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. The problem of wood recognition is compounded with the nonlinearities of the features among the similar wood species. Besides that, large wood databases presented a problem of large processing time especially for online wood recognition system. In view of these problems, we propose the use of fuzzy logic-based pre-classifier as a means of treating uncertainty to improve the classification accuracy of tropical wood recognition system. The pre-classifier serve as a clustering mechanism for the large database simplifying the classification process making it more efficient. The use of the fuzzy logic-based pre-classifier has managed to increase the accuracy of the wood recognition system by 4 % and reduce the processing time for training and testing by more than 75 % and 26 % respectively.

Keywords

Wood species recognition system Pattern recognition  Fuzzy logic Wood pores Texture 

Notes

Acknowledgments

The authors would like to thank Malaysian Ministry of Science, Technology and Innovation (MOSTI) for funding this research through Technofund research grant (TF0106C213). The authors also would like to thank Forest Research Institute of Malaysia (FRIM) for providing us with the wood samples.

References

  1. 1.
    Menon, P.K.B., Sulaiman, A., Choon, L.S.: Structure and identification of Malayn woods. Malayan Forest Research Records. Forest Research Institute Malaysia (1993)Google Scholar
  2. 2.
    Piuri, V., Scotti, F.: Design of an automatic wood types classification system by using fluorescence spectra. IEEE Trans. Syst. Man Cybern. 40(3), 358–366 (2010)CrossRefGoogle Scholar
  3. 3.
    Khalid, M., Lew, Y.L., Yusof, R., Nadaraj, M.: Design of an intelligent wood species recognition system. Int. J. Simul. Syst. Sci. Technol. 9(3), 9–19 (2008)Google Scholar
  4. 4.
    Khairuddin, U., Yusof, R., Khalid, M., Cordova, F.: Optimized feature selection for improved tropical wood species recognition system. ICIC Express Lett. Part B Appl. Int. J. Res. Surv. 2(2), 441–446 (2011)Google Scholar
  5. 5.
    Martins, J., Oliveira, L.S., Nisgoski, S., Sabourin, R.: A database for automatic classification of forest species. Mach. Vis. Appl. Online First\(^{TM}\) (2012)Google Scholar
  6. 6.
    Lin, L., Luo, P., Chen, X., Zeng, K.: Representing and recognizing objects with massive local image patches. Pattern Recognit. 45(1), 231–240 (2012)CrossRefzbMATHGoogle Scholar
  7. 7.
    Lin, L., Wu, T., Porway, J., Xu, Z.: A stochastic graph grammar for compositional object representation and recognition. Pattern Recognit. 42(7), 1297–1307 (2009)CrossRefzbMATHGoogle Scholar
  8. 8.
    Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Ishibuchi, H., Nakashima, T.: Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems. IEEE Trans. Syst. Man Cybern. Part B 29(5), 601–618 (1999)Google Scholar
  10. 10.
    Schaefer, G., Zavisek, M., Nakashima, T.: Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recognit. 42(6), 1133–1137 (2009)CrossRefGoogle Scholar
  11. 11.
    Zhenjiang, M., Gandelin, M.H., Baozong, Y.: An OOPR-based rose variety recognition system. Eng. Appl. Artif. Intell. 19, 79–101 (2006)CrossRefGoogle Scholar
  12. 12.
    Bombardier, V., Schmitt, E.: Fuzzy rule classifier: capability for generalization in wood color recognition. Eng. Appl. Artif. Intell. 23, 978–988 (2010)CrossRefGoogle Scholar
  13. 13.
    Beritelli, F., Casale, S., Ruggeri, G.: New results in fuzzy pattern classification of background noise. Proc. Int. Conf. Signal Process. 3, 1483–1486 (2000)CrossRefGoogle Scholar
  14. 14.
    Su, T.-L., Chang, L.-S., Kung, F.-C.: Intelligent computerized fabric texture recognition system by using Grey-based neural fuzzy clustering. In: International Conference on Wavelet Analysis and Pattern Recognition, 2009. ICWAPR (2009)Google Scholar
  15. 15.
    Laboid, S., Boucherit, M.S., Guerra, T.M.: Adaptive fuzzy control of a class of MIMO nonlinear systems. Fuzzy Sets Syst. 151, 59–77 (2005) Google Scholar
  16. 16.
    Sun, Y.L., Er, M.J.: Hybrid fuzzy control of robotics systems. IEEE Trans. Fuzzy Syst. 12(6), 755–765 (2004)CrossRefGoogle Scholar
  17. 17.
    Beka Be Nguema, M., Kolski, C., Malvache, N., Waroux, D.: Design of a human-error-tolerant interface using fuzzy logic. Eng. Appl. Artif. Intell. 13, 279–292 (2000)CrossRefGoogle Scholar
  18. 18.
    Tuceryan, M., Jain, A.K.: Texture analysis. In: The Handbook of Pattern Recognition and Computer Vision (2nd edn.), pp. 207–248. World Scientific Publishing Co., Singapore (1998)Google Scholar
  19. 19.
    Qin, X., Yang, Y.H.: Similarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 326–333 (2004)Google Scholar
  20. 20.
    Qin, X., Yang, Y.H.: Aura 3D textures. IEEE Trans. Vis. Comput. Graph. 13(2), 379–389 (2007)CrossRefGoogle Scholar
  21. 21.
    Wang, J.P., Jheng, Y.C., Huang, G.M., Chien, J.H.: Artificial neural network approach to authentication of coins by vision-based minimization. Mach. Vis. Appl. 22, 87–98 (2011)CrossRefGoogle Scholar
  22. 22.
    Huang, C.L., Huang, W.Y.: Sign language recognition using model-based tracking and a 3D Hopfield neural network. Mach. Vis. Appl. 10, 292–307 (1998)CrossRefGoogle Scholar
  23. 23.
    Aitkenhead, M.J., McDonald, A.J.S.: A neural network face recognition system. Eng. Appl. Artif. Intell. 16, 167–176 (2003)CrossRefGoogle Scholar
  24. 24.
    Foo, S.Y., Stuart, G., Harvey, B., Meyer-Baese, A.: Neural network based EKG pattern recognition. Eng. Appl. Artif. Intell. 15, 253–260 (2002)CrossRefGoogle Scholar
  25. 25.
    Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C 30(4), 451–462 (2000)CrossRefGoogle Scholar
  26. 26.
    Castellani, M., Rowlands, H.: Evolutionary artificial neural network design and training for wood veneer classification. Eng. Appl. Artif. Intell. 22, 732–741 (2009)CrossRefGoogle Scholar
  27. 27.
    Jordan, R., Feeney, F., Nesbit, N., Evertsen, J.A.: Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonics 36, 219–222 (1998)CrossRefGoogle Scholar
  28. 28.
    Lepisto, L., Kunttu, I., Visa, A.: Rock image classification based on \(k\)-nearest neighbor voting. IEE Proc. Vis. Image Signal Process. 153(4), 475–482 (2006)CrossRefGoogle Scholar
  29. 29.
    Amornraksa, T., Tachaphetpiboon, S.: Fingerprint recognition using DCT features. Electron. Lett. 42(9), 522–523 (2006)CrossRefGoogle Scholar
  30. 30.
    Golipour, L., O’Shaughnessy, D.: Context-independent phoneme recognition using a \(k\)-nearest neighbor classification approach. In: IEEE International Conference On Acoustics, Speech And, Signal Processing, pp. 1341–1344. (2009)Google Scholar
  31. 31.
    Ng, H., Tong, H.L., Tan, W.H., Yap, T.V., Abdullah, J.: Gait classification with different covariate factors. In: International Conference on Computer Applications and Industrial Electronics, pp. 463–467 (2010)Google Scholar
  32. 32.
    Jian, H., Zhongdi, C., Qiuhong, Z.: Research and implement of Chinese text classifier based on Naïve Bayes method. In: Sixth International Conference on Semantics, Knowledge and Grids, pp. 426–428. (1010)Google Scholar
  33. 33.
    Daschiel, H., Datcu, M.: Information mining in remote sensing image archives: system evaluation. IEEE Trans. Geosci. Remote Sens. 43(1), 188–199 (2005)CrossRefGoogle Scholar
  34. 34.
    Buch, N., Orwell, J., Velastin, S.A.: Detection and classification of vehicles for urban traffic scenes. In: IET International Conference on Visual Information Engineering, pp. 182–187 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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