Colour Extraction of Agarwood Images for Fuzzy C-Means Classification

Mohamad Razi MAD AMIN, Siti Khairunniza BEJO, Wan Ishak WAN ISMAIL, Shamsiah MASHOHOR


Colour is one of the quality features used in determining agarwood quality and grade. This research investigates the relationship of agarwood physical colour properties with its price. Colour features of agarwood images taken from Red, Green, Blue (RGB), Hue, Saturation, Intensity (HSI) and Commission Internationale de l'Eclairage standard L,*a,*b colorspace (CIELAB) has been extracted by Fuzzy C-Means (FCM) classification. The performance of these colorspaces has been determined using five cluster validity indices. One hundred and forty agarwood images consisting of seven different prices have been analyzed. From the experiment, it has been shown that CIELAB colorspace with four numbers of clusters gave more consistent and accurate results compared to the others. It also gave a significant relationship when tested using analysis of variance (ANOVA) and Duncan Multiple Range Test (DMRT). The method performs best when classifying lower price agarwood with component L for RM250 and RM800, b for RM350 and RM2500 while a for RM900. Overall, the proposed method proved that there is a significant relationship between agarwood price and its physical colour properties, which thus shows that the image processing has an enormous potential to be used in the agarwood chips grading task for the future development.


Agarwood, colorspace, fuzzy c-means, classification, cluster validity

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LSL Chua. Agarwood (Aquilaria Malaccensis) in Malaysia, Forest Research Institute Malaysia, 2008.

TE Asia-Taipei and TS Asia. The Trade and Use of Agarwood in Taiwan, Province of China. CITES; 2005.

T Soeharto and AC Newton. Conservation and sustainable use of tropical trees in the genus Aquilaria II. The impact of gaharu harvesting in Indonesia. Biol. Conservat. 2001; 97, 29-41.

T Soeharto and AC Newton. The gaharu trade in Indonesia: Is it sustainable? Econ. Bot. 2002; 56, 271-84.

F Zich and J Compton. The Final Frontier: Towards Sustainable Management of Papua New Guinea's Agarwood Resource. In: TRAFFIC Oceania and the WWF South Pacific Programme, 2001.

A Barden, NA Anak, T Mulliken and M Song. Heart of the Matter: Agarwood Use and Trade and CITES Implementation for Aquilaria Malaccensis. TRAFFIC International Publication, 2000.

K Chakrabarty, A Kumar and V Menon. Trade in Agarwood. In: TRAFFIC India, New Delhi, 1992.

W Kurdthongmee. Colour classification of rubberwood boards for fingerjoint manufacturing using a SOM neural network and image processing. Comput. Electron. Agr. 2008; 64, 85-7.

A Abdullah, NKN Ismail, TAA Kadir, JM Zain, NA Jusoh and NM Ali. Agar wood grade determination system using image processing technique. In: Proceedings of Electrical Engineering and Informatics. Institut Teknologi Bandung, Indonesia. 2007, p. 427-9.

DG Donovan and RK Puri. Learning from traditional knowledge of non-timber forest products: Penan benalui and the autecology of Aquilaria in Indonesian Borneo. Ecol. Soc. 2004; 9, Art 3.

NR Pal and SK Pal. A review on image segmentation techniques. Pattern Recognit. 1993; 26, 1277-94.

Y Chahir and A Elmoataz. Skin-color detection using fuzzy clustering. In: Proceedings of International Symposium on Control, Communications, and Signal Processing. Marrakech, Morocco, 2006.

CFJ Kuo, CY Shih, CY Kao and JY Lee. Color and pattern analysis of printed fabric by an unsupervised clustering method. Textil. Res. J. 2005; 75, 9-12.

Z Ronghua, C Hongwu, Z Xiaoting, P Ruru and L Jihong. Unsupervised color classification for yarn-dyed fabric based on FCM algorithm. In: Proceedings of International Conference on Artificial Intelligence and Computational Intelligence, Sanya, China. 2010, p. 497-501.

JY Kang, LQ Min, QX Luan, X Li and JZ Liu. Dental plaque quantification using FCM-based classification in HSI color space. In: Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China. 2008, p. 78-81.

T Saikumar, P Yugander, P Sreenivasa and B Smitha. Colour based image segmentation using fuzzy c-means clustering. In: Proceedings of International Conference on Computer and Software Modeling, Singapore. 2011, p. 180-5.

G Padmavathi, Mr Muthukumar. Image segmentation using fuzzy c means clustering method with thresholding for underwater images. Int. J. Adv. Network. Appl. 2010; 2, 514-8.

S Chuai-Aree, C Lursinsap, P Sophatsathit and S Siripant. Fuzzy C-means: a statistical feature classification of text and image segmentation method. Int. J. Uncertainty, Fuzziness and Knowledge 2001; 9, 661-71.

VEC Ghaleh and A Behrad. Lip contour extraction using RGB color space and fuzzy c-means clustering. In: Proceedings of the 9th International Conference on Cybernetic Intelligent Systems, Reading, UK. 2010.

A Sopharak, B Uyyanonvara and S Barman. Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering. Sensors 2009; 9, 2148-61.

NR Pal and JC Bezdek. On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 1995; 3, 370-9.

KL Wu, MS Yang and JN Hsieh. Robust cluster validity indexes. Pattern Recognit. 2009; 42, 2541-9.

Y Zhang, W Wang, X Zhang and Y Li. A cluster validity index for fuzzy clustering. Inform. Sci. 2008; 178, 1205-13.

CY Yen and KJ Cios. Image recognition system based on novel measures of image similarity and cluster validity. Neurocomputing 2008; 72, 401-11.

W Wang and Y Zhang. On fuzzy cluster validity indices. Fuzzy Set. Syst. 2007; 158, 2095-117.

HD Cheng, XH Jiang, Y Sun and J Wang. Color image segmentation: advances and prospects. Pattern Recognit. 2001; 34, 2259-81.

RC Gonzalez and RE Woods. Digital Image Processing. 2nd ed. Prentice Hall, New Jersey, 2002, p. 190.

KL Wu and MS Yang. A cluster validity index for fuzzy clustering. Pattern Recognit. Lett. 2005; 26, 1275-91.

CH Chou, MC Su and E Lai. A new cluster validity measure and its application to image compression. Pattern Anal. Appl. 2004; 7, 205-15.

S Thilagamani and N Shanthi. Literature survey on enhancing cluster quality. Int. J. Comput. Sci. Eng. 2010; 2, 1999-2002.

KA Gomez and AA Gomez. Statistical Procedures for Agricultural Research. 2nd ed. John Wiley & Sons, New York, 1984, p. 680.


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