1Jing Yi Tou, 1Yong Haur Tay, 2Phooi Yee Lau

1Computer Vision and Intelligent Systems (CVIS) Group,

Universiti Tunku Abdul Rahman (UTAR), Malaysia2Hanyang University, Republic of Korea

E-mails: {toujy,tayyh}@utar.edu.my, laupy@hanyang.ac.kr

A COMPARATIVE STUDY FOR TEXTURE

CLASSIFICATION TECHNIQUES ON WOOD

SPECIES RECOGNITION PROBLEM

The 5th International Conference on Natural Computation and the 6th International

Conference on Fuzzy Systems and Knowledge Discovery (ICNC’09-FSKD’09)

OUTLINE

• Introduction

• Feature Extraction

• Classifier

• Experiments

• Conclusion

• Acknowledgement

• References

Introduction

Introduction

• What is wood species recognition?

• Wood species recognition is important as different species

of wood has different characteristics and features, these

might affect the results or products when they are used for

different purposes. In the industry, wood material must be

examined before it is selected to be used to produce a

product.

• Applications: construction, conservation, archaeology,

forensic investigation, and etc.

Introduction

Pear Tree

(Pyrus calleryana)

Chinese Fan Palm

(Livistona chinensis)

Coconut Palm

(Cocos nucifera)

Apple Tree (Malus

domestica)

?

?

Wood Cross-section Surface

Sepetir (Species A)

Sindora coriacea

Punah (Species B)

Tetramerista glabra

(Species C)

Myristica iners

Merbau (Species D)

Intsia palembanica

Ramin (Species E)

Gonystylus bancanus

(Species F)

Artocarpus kemando

References:

Menon, P. K. B. (revised by Sulaiman, A. and Lim, S. C.). (1993). Structure and identification of Malayan woods: Malayan forest records no. 25. Malaysia: Forest Research Institute of Malaysia.

?

Texture

• Texture is “the variation of data at scales smaller than the

scales of interest”

• Textures are generally pattern that we can percept from the

surface of an object that helps us recognize what it is and

to predict the properties that it has, such as a smooth or

rough, hard or soft.

References:Petrou, M. and Sevilla, P. G. (2006). Image processing dealing with texture. West Sussex, England: John Wiley & Sons.

Texture

• Applicable in many pattern recognition applications:

• Wood species recognition, face detection, document

analysis, etc.

References:

Tuceryan, M. and Jain, A. K. (1998). Texture analysis. In: The Handbook of Pattern Recognition and Computer Vision. 2nd Edition. (pp 207-248). Singapore: World Scientific Publishing.

Wood Species Recognition [Manual]

• Wood species recognition is performed on the cross-

section surface of the wood samples because it contains

most characteristics

• Experts uses a hand lens (10 x) to view the surface and

identify it with the characteristics observed

Cross-Section Surface

Motivation

• Introducing potential texture analysis algorithm:

• Texture classification is useful to solve various pattern

recognition problems

• Consistent time for the recognition process:

• Experts may take up to days or weeks to determine the

species of a wood due to the number of species and

difficulties to differentiate certain species

• Demand from the market:

• Could be used in furniture manufacture, construction,

immigration, forensics, wood studying firms and etc

Objectives

• To recognize the wood species through the cross section

surface of the wood samples through different texture

classification techniques.

• To compare the performance of each texture classification

technique.

Feature Extraction

1. Grey Level Co-occurrence (GLCM)

• GLCM shows relationship

between grey-scaled pixels

values of the image.

• Step 1:

Select direction and spatial

distance to generate the GLCM

• Step 2:

Generate the GLCM according to

the selected parameters

Reference:

Haralick, R. M., Shanmugam, K. and Dinstein, L. (1973). Textural features for image classification. IEEE Transactions on System, Man, and Cybernectics, 3, 610-621.

Directions and Spatial Distances

Generated Raw GLCMs

Images

Images

1. GLCM (cont.)

Reference:Haralick, R. M., Shanmugam, K. and Dinstein, L. (1973). Textural features for image classification. IEEE Transactions on System, Man, and Cybernectics, 3, 610-621.

• Step 3:

Use textural feature functions on

the GLCM to extract features

Normalization:

Textural Features

2. Gabor Filters

• Extract features by analysing the frequency domain of the image

• Step 1:

Create Gabor filters according to the radial center frequency and

orientation.

Reference:W. H. Yap, M. Khalid, and R. Yusof, “Face Verification with Gabor Representation and Support Vector Machines”, IEEE Proc. of the First Asia International Conference on Modeling and

Simulation, 2007, pp. 451-459. c

Imaginary Part for Gabor filter

Real Part for Gabor filter

2. Gabor Filters (cont.)

• Step 2:

FFT -> convolution -> IFFT

• Step 3:

Principal Component Analysis (PCA) to reduce feature

dimension

- Singular Value Decomposition (SVD)Reference:W. H. Yap, M. Khalid, and R. Yusof, “Face Verification with Gabor Representation and Support Vector Machines”, IEEE Proc. of the First Asia International Conference on Modeling and

Simulation, 2007, pp. 451-459. c

Orientations

Radial Center

Frequencies

Images filtered by the Gabor filters

3. Combined GLCM and Gabor Filters

• Texture classification techniques are often combined

• The GLCM features are combined with the Gabor features

by directly appending the Gabor features after the GLCM

features.

• Proven to be better than either GLCM or Gabor filters in

some applications.

Reference:J.Y. Tou, Y.H. Tay, P.Y. Lau, “Gabor Filters and Grey-level Co-occurrence Matrices in Texture Classification”, MMU International Symposium on Information and Communications

Technologies, Petaling Jaya, 2007.

J.A.R. Recio, L.A.R. Fernandez, and A. Fernandez-Sarria, “Use of Gabor filters for texture classification of digital images”, 2005.

4. Covariance Matrix

• The covariance matrix is used to represent the co-variance

between the Gabor filters

• It reduces the dimension of the Gabor features

• The covariance matrices did not lie on Euclidean space,

hence Forstners and Moonen’s distance is used

Reference:J. Y. Tou, Y. H. Tay, and P. Y. Lau, “Gabor Filters as Feature Images for Covariance Matrix on Texture Classification Problem”, Lecture Notes in Computer Science - Proc. 15th International

Conference on Neural Information Processing, vol. 5507, Nov 2008, pp. 745-751.

Classifier

1. k-Nearest Neighbor (k-NN)

• Winning class decided by the k selected best neighbors

from the query sample

2. Verification-based Recognition: Feature

• GLCM is used as the features

• Eight directions are calculated for each test sample rather

than rotating the input images

Reference:J.Y. Tou, Y.H. Tay, and P.Y. Lau, “Rotational Invariant Wood Species Recognition through Wood Species Verification”, Proc. 1st Asian Conference on Intelligent Information and Database

Systems, Apr. 2009, pp. 115-120.

Eight Directions for GLCM Generation

2. Verification-based Recognition: Verification

• Each training samples are used as templates

• The energy value is calculated as the distance between all

training samples

• A threshold value is calculated for each template based on

the same species

• The energy value for the test sample is the minimum energy

obtained for the eight directions

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360...0

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Reference:J.Y. Tou, Y.H. Tay, and P.Y. Lau, “Rotational Invariant Wood Species Recognition through Wood Species Verification”, Proc. 1st Asian Conference on Intelligent Information and Database

Systems, Apr. 2009, pp. 115-120.

2. Verification-based Recognition: Recognition

• For each test sample, the species with most winning

templates accepted is the winning species

Reference:J.Y. Tou, Y.H. Tay, and P.Y. Lau, “Rotational Invariant Wood Species Recognition through Wood Species Verification”, Proc. 1st Asian Conference on Intelligent Information and Database

Systems, Apr. 2009, pp. 115-120.

Experiments

Experimental Dataset

• Macroscopic wood images from CAIRO (Centre of Artificial

Intelligence and Robotics), Universiti Teknologi Malaysia

(UTM)

• 6 species of wood

• 90 training samples for each species

• 10 testing samples for each species

1. Sesendok (Endospermum

malaccense)

2. Keledang (Artocarpus

kemando)

3. Nyatoh (Palaquium

impressinervium)

4. Punah (Tetramerista

glabra)

5. Ramin (Gonystylus

bancanus)

6. Melunak (Pentace triptera)

• Best recognition rate: 85.00% for Gabor-filter-based

Covariance Matrix

Comparison of Different Techniques

Texture Classification Techniques Accuracy (%)

GLCM features 76.67

Raw GLCM 78.33

Gabor features 73.33

Combined GLCM and Gabor features 76.67

Gabor filter-based Covariance Matrix 85.00

Verification-based Recognition 78.33

Findings

• Strong difference between training and testing samples of

sixth species (Melunak, Pentace triptera)

• The train samples (left) has more obvious white lines and

black dots but both characteristics are not obvious in the

test samples (right)

Melunak (Training Sample) Melunak (Testing Sample)

Findings

• Strong similarity between different species

• Samples from Punah, Tetramerista glabra (left) and Nyatoh,

Palaquium impressinervium (right) are both sharing similar

patterns of black pores and bright markings occurring

along the rays.

Punah (Tetramerista glabra) Nyatoh (Palaquium impressinervium)

Findings

• Defects causes misclassification in first species (Sesendok,

Endospermum malaccense)

• Certain image has a form of defects which are whitish

shown in the circled areas

Defects

Conclusion

Conclusion

• Similar findings compared to general texture classification:

• Raw GLCM outperform GLCM features.

• Gabor filters produced poorer result.

• Combine GLCM and Gabor features outperform both

when used independently.

• Gabor filter-based covariance matrix performed the best

(85%).

• Wood species recognition is more challenging that general

texture classification due to the natural similarity of

textures among wood species.

Future Works

• Proposed embedded device for wood species recognition

Acknowledgement

Acknowledgement

• The authors would like to thank Y. L. Lew and the Centre for

Artificial Intelligence and Robotics (CAIRO) of Universiti

Teknologi Malaysia (UTM) for sharing the wood images.

• This research is partly funded by Malaysian MOSTI

ScienceFund 01-02-11-SF0019.

References

References

1. Y.L. Lew, Design of an Intelligent Wood Recognition System for the Classification of Tropical

Wood Species, M. E. Thesis, Universiti Teknologi Malaysia, 2005.

2. J.Y. Tou, P.Y. Lau, Y.H. Tay, “Computer Vision-based Wood Recognition System”, Proc. Int’l

Workshop on Advanced Image Technology, Bangkok, 2007, pp. 197-202.

3. J.Y. Tou, Y.H. Tay, P.Y. Lau, “Gabor Filters and Grey-level Co-occurrence Matrices in Texture

Classification”, MMU International Symposium on Information and Communications

Technologies, Petaling Jaya, 2007.

4. J.Y. Tou, Y.H. Tay, and P.Y. Lau, “Rotational Invariant Wood Species Recognition through

Wood Species Verification”, Proc. 1st Asian Conference on Intelligent Information and

Database Systems, Apr. 2009, pp. 115-120.

5. M. Tuceryan, A.K. Jain, “Texture Analysis”, in: C.H. Chen, L.F. Pau, P.S.P Wang (eds.), The

Handbook of Pattern Recognition and Computer Vision (2nd Edition), World Scientific

Publishing Co, 1998.

6. Y.Q. Chen, Novel Techniques for Image Texture Classification, PhD Thesis, University of

Southampton, United Kingdom, 1995.

7. R.M. Haralick, K. Shanmugam, I. Dinstein, “Textural Features for Image Classification”,

IEEE Transactions on Systems, Man and Cybernatics, 1973, pp. 610-621.

8. M. Petrou, P.G. Sevilla, Image Processing: Dealing with Texture, Wiley, 2006.

9. J.A.R. Recio, L.A.R. Fernandez, and A. Fernandez-Sarria, “Use of Gabor filters for texture

classification of digital images”, 2005.

10. O. Tuzel, F. Porikli, and P. Meer, “Region Covariance: A Fast Descriptor for Detection and

Classification”, European Conference on Computer Vision, vol. 1, 2006, pp. 697-704.

Q & A

Thank You!