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

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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.

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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.

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Authors and Affiliations

  1. Center for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Rubiyah Yusof & Marzuki Khalid

  2. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Anis Salwa Mohd Khairuddin

Authors
  1. Rubiyah Yusof
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  2. Marzuki Khalid
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  3. Anis Salwa Mohd Khairuddin
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Correspondence to Rubiyah Yusof.

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Yusof, R., Khalid, M. & Mohd Khairuddin, A.S. Fuzzy logic-based pre-classifier for tropical wood species recognition system. Machine Vision and Applications 24, 1589–1604 (2013). https://doi.org/10.1007/s00138-013-0526-9

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  • DOI: https://doi.org/10.1007/s00138-013-0526-9

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