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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.5, No.10, Aug. 2013

Image Identification Based on Shape and Color Descriptors and Its Application to Ornamental Leaf

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Author(s)

Kohei Arai, Indra Nugraha Abdullah, Hiroshi Okumura

Index Terms

Identification, Dyadic wavelet, Zernike moments, HSV, SVM, leaf, overlapping

Abstract

Human has a duty to preserve the nature, preserving the plant is one of the examples. This research has an emphasis on ornamental plant that has functionality not only as ornament but also as medicine. Although in Indonesia, in general this plant is cultivated in front of the house; only few people know about its medicinal function. Considering this easiness to obtain and its medicinal function, this plant has to be an initial treatment or option towards full chemical-based medicines. This research proposes a system which able to identify properly ornamental plant from its leaf utilizing its shape or color features. Shape descriptor represented by Dyadic Wavelet Transformation and Zernike Complex Moment, and HSV-based color histogram as color descriptor. This research provides benefit of these three methods to solve various test aspects. It was obtained 81.77% of overall average-testing performance.

Cite This Paper

Kohei Arai, Indra Nugraha Abdullah, Hiroshi Okumura"Image Identification Based on Shape and Color Descriptors and Its Application to Ornamental Leaf", IJIGSP, vol.5, no.10, pp.1-8, 2013.DOI: 10.5815/ijigsp.2013.10.01

Reference

[1]IUCN 2012. Numbers of threatened species by major groups of organisms (1996–2012). <http://www.iucnredlist.org/documents/summarystatistics/2012_2_RL_Stats_Table_1.pdf >. Downloaded on 21 June 2013.

[2]Chapman A. D. Numbers of Living Species in Australia and the World. Australian Government, Department of the Environment, Water, Heritage, and the Arts. Canberra, Australia, 2009.

[3]Zhang D., Lu G. Review of shape representation and description techniques. Pattern Recognition, 2004, vol.37, 1-19.

[4]Abdukirim T., Nijima K., Takano S. Lifting dyadic wavelets for denoising. Proceedings of the Third International Workshop on Spectral Methods and Multirate Signal Processing (SMMSP), 2003, 147-154.

[5]Minamoto T., Tsuruta K., Fujii S. Edge-preserving image denoising method based on dyadic lifting schemes. IPSJ Transactions on Computer Vision and Applications, 2010, vol. 2, 48-58.

[6]Mallat S. A wavelet tour of signal processing. Academic Press. 1998.

[7]Tahmasbi A., Saki F., Shokouhi S. B. Classification of benign and malignant masses based on zernike moments. Computers in Biology and Medicine, 2011, vol. 41, 726–735.

[8]S.K. Hwang, W.Y. Kim, A novel approach to the fast computation of Zernike moments, Pattern Recognition, 2006, vol.39, 2065–2076.

[9]Ch. Y. Wee1, R. Paramesran, F. Takeda. Fast computation of zernike moments for rice sorting system. Proceedings of the IEEE, International Conference on Image Processing (ICIP), 2007, pp. VI-165–VI-168. 

[10]B. Fu, J. Liu, X. Fan, Y. Quan. A hybrid algorithm of fast and accurate computing zernike moments. Proceedings of the IEEE, International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2007, pp. 268–272.

[11]Park Jinkyu, Hwang Eenjun, Nam Yunyoung. Utilizing venation features for efficient leaf image retrieval. The Journal of System and Software, 2008, Vol.81, 71-82. 

[12]Du Jin-Xiang, Wang Xiao-Feng, Zhang Guo-Jun. Leaf Shape based plant species recognition. Applied Mathematics and Computation, 2007, vol. 185, 883-893. 

[13]Wang Xiao-Feng, et.al. Classification of plant images with complicated background. Applied Mathematics and Computation, 2008, vol.205, 916-926. 

[14]Hartati Sri. Tanaman hias berkhasiat obat. IPB Press. 2011. (In Indonesian)

[15]C.H. Theh, R.T. Chin, On image analysis by the methods of moments, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, vol. 4(10), 496–513.

[16]Ben-Hur Asa, Weston Jason. A user's guide to support vector machine. Data Mining Techniques for the Life Science, 223-239, Humana Press, 2010. 

[17]Hsu Chih-Wei, Chang Chih-Chuang, Lin Chih-Jen. A practical guide to support vector classification. Department of Computer Science National Taiwan University, Taiwan, 2010. 

[18]Starck Jean-Luc, Murtagh Fionn, Fadili Jalal M. Sparse image and signal processing. Cambridge University Press. 2010.

[19]G.Stockman and L.Shapiro. Computer Vision. Prentice Hall, 2001.