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

• Published in: Machine vision and applications 2013-11, Vol.24 (8), p.1589-1604
• Main Authors: Yusof, Rubiyah, Khalid, Marzuki, Mohd Khairuddin, Anis Salwa
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Classification; Classifiers; Clustering; Communications Engineering; Computer Science; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Fuzzy logic; Fuzzy systems; Image Processing and Computer Vision; Information systems. Data bases; Memory organisation. Data processing; Networks; On-line systems; Original Paper; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Software; Species classification; Tropical forests; Vision systems
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-013-0526-9

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.