Abstract
Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant species. Plant classification based on leaf images is a basic research of botanical area and agricultural production. Due to the high nature complexity and high dimensionality of leaf image data, dimensional reduction algorithms are useful and necessary for such type of data analysis, since it can facilitate fast classifying plants, and understanding and managing plant leaf features. Supervised locally linear embedding (SLLE) is a powerful feature extraction method, which can yield very promising recognition results when coupled with some simple classifiers. In this paper, a semi-SLLE is proposed and is applied to plant classification based on leaf images. The experiment results show that the proposed algorithm performs very well on leaf image data which exhibits a manifold structure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alan, K.K., Philip, A., Fay, et al.: Rainfall Variability, Carbon Cycling, and Plant Species Diversity In A Mesic Grassland. Science 298, 2202–2205 (2002)
Alan Pounds, J., Robert, P.: Ecology: Clouded Futures. Nature 427, 107–109 (2004)
Du, J.X., Wang, X.F., Zhang, G.J.: Leaf shape based plant species recognition. Applied Mathematics and Computation 185, 883–893 (2007)
Ye, Y., Chen, C., Li, C.T., Fu, H., Chi, Z.: A computerized plant species recognition system. In: Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong (October 2004)
Miao, Z., Gandelin, M.H., Yuan, B.: An oopr-based rose variety recognition system. Engineering Applications of Artificial Intelligence 19 (2006)
de Oliveira Plotzede, R., Falvo, M., Pdua, J.G., Bernacci, L.C., Oliveira, M.L.C., Bruno, O.M.: Leaf shape analysis using the multiscale minkowski fractal dimension, a new morphometric method: a study with passifliora (passifloraceae). Canada Journal of Botany 83 (2005)
de Ridder, D., Duin, R.P.W.: Locally linear embedding for classification. Technical Report PH-2002-01, Pattern Recognition Group, Dept. of Imaging Science & Technology, Delft University of Technology, Delft, The Netherlands (2002)
Kouropteva, O., Okun, O., Pietikäinen, M.: Selection of the optimal parameter value for the locally linear embedding algorithm. In: Proc. of the 1st Int. Conf. on Fuzzy Systems and Knowledge Discovery, Singapore, pp. 359–363 (2002)
Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (1990)
Wang, Y.H., Makedon, F.S., Ford, J.C., Pearlman, J.: HykGene: A Hybrid Approach for Selecting Marker Genes for Phenotype Classification using Microarray Gene Expression Data. Bioinformatics 21(8), 1530–1537 (2005)
de Silva, V., Tenenbaum, J.B.: Global Versus Local Methods in Nonlinear Dimensionality Reduction. NIPS, 705–712 (2002)
Huang, H., Li, J.W., Feng, H.L.: Face Recognition on Semi-supervised Manifold Learning. Computer Science 35(12), 220–222 (2008)
Lee, C.L., Chen, S.Y.: Classification for Leaf Images. In: 16th IPPR Conference on Computer Vision, Graphics and Image Processing, vol. 8, pp. 17–19 (2003)
Shi, C., Chen, L.H.: Feature dimension reduction for microarray data analysis using locally linear embedding. APBC162 16, 1–7 (2004)
Pillati, M., Viroli, C.: Supervised Locally Linear Embedding for Classification: An Application to Gene Expression Data Analysis. In: Proceedings of 29th Annual Conference of the German Classification Society (GfKl 2005), pp. 15–18 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, S., Chau, KW. (2009). Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_100
Download citation
DOI: https://doi.org/10.1007/978-3-642-04070-2_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04069-6
Online ISBN: 978-3-642-04070-2
eBook Packages: Computer ScienceComputer Science (R0)