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

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. Besid...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-013-0526-9