Authors
Mohd Iz’aan Paiz Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah
Yusof
Corresponding Author
Mohd Iz’aan Paiz Zamri
Available Online 1 December 2016.
DOI
https://doi.org/10.2991/jrnal.2016.3.3.1
Keywords
image classification, wood texture, wood species, support vector machine,
pattern recognition.
Abstract
An automated wood species recognition system is designed to perform wood
inspection at custom checkpoints in order to avoid illegal logging. The
system that includes image acquisition, feature extraction and classification
is able to classify the 52 wood species. There are 100 images taken from
the each wood species is then divided into training and testing samples
for classification. In order to differentiate the wood species precisely,
an effective feature extractor is necessary to extract the most distinguished
features from the wood surface. In this research, an Improved Basic Grey
Level Aura Matrix (I-BGLAM) technique is proposed to extract 136 features
from the wood image. The technique has smaller feature dimension and is
rotational invariant due to the considered significant feature extract
from the wood image. Support vector machine (SVM) is used to classify the
wood species. The proposed system shows good classification accuracy compared
to previous works.
Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).