Journal of Robotics, Networking and Artificial Life, Vol. 3, No. 3 (December 2016), 140-143

Wood Species Recognition System based on Improved Basic Grey Level Aura Matrix asfeature extractor
Mohd Iz’aan Paiz Zamri 1, Anis Salwa Mohd Khairuddin1, Norrima Mokhtar 1, Rubiyah Yusof 2
1

Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
2Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur Malaysia
E-mails: izanpaiz@gmail.com, anissalwa@um.edu.my
www.um.edu.my, www.fk.um.edu.my,www.mjiit.utm.my

Abstract
An automated wood species recognition system is designed to perform wood inspection at custom checkpoints inorder to avoid illegal logging. The system that includes image acquisition, feature extraction and classification isable to classify the 52 wood species. There are 100 images taken from the each wood species is then divided intotraining 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, anImproved 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.
Keywords: image classification, wood texture, wood species, support vector machine, pattern recognition.

1. Introduction
Traditional wood identification system which is based
solely on human expertise is not practical nowadays.
Several automatic wood species recognition have beendeveloped based on spectral analysis and imageanalysis. The wood species identification based on thespectrum analysis involves a lot of time, money andspecialists as shown in the previous research1-6. In
addition, the technique is not suitable to be applied inreal life applications by non-experts and is morepractical to do it in the laboratory rather than in thefield. On the other hand, the image-based processing
provides a simpler method in classifying the woodspecies. The method involves only the inspection of across section of the wood surface that can be determinedby human naked eye with the aid of magnifying lens.
Refs. 7 and 8 develop an automatic tropical woodspecies recognition system to classify 20 wood species
and 10 wood species respectively. In both works, Grey
Level Co-occurrence Matrix (GLCM) is used as thefeature extractor to extract the features from the wood
texture. After the database is increased to 52 woodspecies the accuracy dropped to 50% indicating that theGLCM in the previous work is inadequate for large
wood database. This is due to the unvaried nature ofmacroscopic anatomy image of the wood, but it contains

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Mohd Iz’aan Paiz Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusofa visual texture - variation of intensities which form
certain repeated arrangements9.
In order to improve the performance of the woodspecies recognition system, a reliable and precisefeature extractor is needed. To correctly extract thewood features from the wood texture and overcome thelimitations of GLCM, a new feature extractor, Improved
Basic Grey Level Aura Matrix (I-BGLAM) is proposed.
Support Vector Machine (SVM) is used as classifier toclassify the wood species based on the feature extracted
from the I-BGLAM. Several phases which consist ofimage acquisition, feature extraction and classification
are proposed as the design of the automated woodrecognition system.
2. Proposed Methodologies
The proposed wood identification system based onimage analysis consists of several parts: image acquisition, feature extraction and classification. The illustration of the proposed system is shown in the Fig. 1.

homomorphic filtering is explained in more detail inRef. 10.
2.2. I-BGLAM feature extractor
In order to overcome the limitations of the conventional
BGLAM technique, an improved BGLAM featureextractor is proposed. Basically, the improved BGLAM
feature extractor is rotational invariant and the size offeature dimension is reduced by considering only thediscriminative features. Besides that, I-BGLAM
features are directly calculated from the grey levelmapping of images which overcome the limitations ofGLCM.
This is done by creating a new characteristic
vector which combine of the occurrence of 00 together,
the occurrences of 01 and 10 together and theoccurrences of 11 together.
[ 𝟔 𝟔 𝟒 𝟒 | 𝟑 𝟖 𝟑 𝟐 | 𝟔 𝟔 𝟑 𝟓 | 𝟔 𝟑 𝟑 𝟒 ] ⇒ [ 𝟐𝟐 𝟑𝟑 𝟏𝟏]

For example,

6 + 3 + 6 + 6 = 21 , 4 + 2 + 5 + 4 = 15
6+4+8+ 3+6+3+3+3 =3

andThis is how a 32 feature vector BGLAM becomes a 3feature vector invariant to image rotation. The numberof I-BGLAM features depends only on the grey levelsegmentation as shown in Eq. (1):Nf =Fig. 1. Proposed wood identification system

2.1. Image acquisition
In image acquisition stage, images of wood surface arecaptured by a specially designed portable camera. The
portable camera has 10 times magnification, which isthe same specification as the magnifier used in manual
wood identification process. The size of each image is
768 × 576 pixels.
The wood images are pre-processed usinghomomorphic filters to enhance the image presentation.
Homomorphic filtering uses a linear filter to do a nonlinear mapping to a different domain and later it ismapped back to the original domain. This algorithm
reduces all unwanted illumination and reflectance on theimage. Image brightness is also normalized, thus
enhancing the contrast of the image. The algorithm ofGL × (GL + 1)2(1)Where Nf is the number of features and GL represents
grey level.
Consequently, the number of features becomes lessat the same time. In this research, the numbers of grey
level experimented is 2, 4, 8, 16. Finally, a grey level of16, which gives 136 features is chosen for the final
experiment because it gives the highest classification
accuracy compared to other grey levels.
2.3. ClassificationThe classification process is performed by training aclassifier model that works by differentiating features of
the class of observed collected data. Support Vector
Machine (SVM) is a useful tool as a learning algorithm
in analyzing and recognizing data. Previous works in

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Wood Species Recognition SystemRefs. 11-17 shows that the implementation of SVM
classifier result to high classification rate.
3. Results and Discussion
The experiment was conducted by using 90 training
samples and 10 test samples for each wood species andis repeated for 30 iterations. From the result obtained(Table 1), the classification accuracy for training
samples for all set showed 100% in accuracy. The
computed training accuracy showed that SVM is able totrain the training model and identify the trained data in
training samples database accurately. The classifier
managed to classify the test samples with an average
accuracyof
99.84 ± 0.23 % for 52 wood species.
Table 1. Average classification accuracy
SetClassification
accuracy (%)

Train

100Test99.84 ± 0.23

The proposed system showed an increment in test
accuracy compared to the previous works done shownin the Table 2.
Table 2. Previous works on wood species recognition system
based on image texture
Ref.

No of species(No ofsamples)

Texture feature(No of features)Classification
accuracy (%)

GLCM (20)Image
segmentation (5)GLCM (20)
GLCM (24),Color (18)
GLCM (24),Color (18)95.00

720 (1949)

177(101)18

5 (250)1911 (347)2022 (1270)

2125(1250)

GLCM (124)

68.40

22
23

10 (1000)
25 (500)

95.00
92.60

24

20 (2010)

2530 (3000)

GLCM (3)
GLCM (44)Gabor filtersGLCM (200)Gabor filtersGLCM (200),
Gabor filters(80),GLCM(20)

93.00
72.00
82.00
80.80

91.00

90.33

4. Conclusion
The classification of features extracted from I-BGLAM
using SVM classifier shows an improvement in theclassification accuracy compared to previous proposedsystems. The features extracted from the wood species
using I-BGLAM as feature extractor are more distinct,
thus improved the accuracy of the wood recognition
system. In future, an advance wood recognition system
is needed to overcome the size of wood database andable to differentiate the wood species that contain more
likely features with each other. A robust feature
extractor that capable to extract the most discriminantfeatures from the wood texture is the aim fordevelopment in future.
Acknowledgements
The authors would like to thank Malaysian Ministry ofHigher Education (MOHE) and University of Malayafor funding this research through High Impact ResearchGrant (UM.C/HIR/MOHE/ENG/16) and BKP Grant(BK047-204). The authors also would like to thankForest Research Institute of Malaysia (FRIM) for
providing us with the wood samples. The first author
would like to thank to Ministry of Higher Education(MOHE) for awarded him My Master scholarship.
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