Abstract
As one of the most important morphological taxonomy features, plant leaf with many strong points has significant influence on research. In this paper, we propose a novel method of plant classification from leaf image set based on wavelet transforms and support vector machines (SVMS). Firstly, the leaf images are converted into the time-frequency domain image by wavelet transforms without any further preprocessing such as image enhancement and texture thinning, and then feature extraction vector is conducted. Then the effectiveness of the proposed method is evaluated by the classification accuracy of SVM classifier. The experimental results about the data set with 300 leaf images show that the method has higher recognition rate and faster processing speed.
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References
Du, J.X., Wang, X.F., Zhang, G.J.: Leaf shape based plant species recognition. Applied Mathematics and Computation 185 (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, P., Falvo, R.M., Pdua, J.G., Bernacci, L.C.M., Vieira, L.C.G., Oliveira, C.X., 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)
Dallwitz, M.J.: A general system for coding taxonomic descriptions. Taxon 29 (1980)
Fu, H., Chi, Z., Feng, D., Song, J.: Machine learning techniques for ontology-based leaf classification. In: IEEE 2004 8th International Conference on Control, Automation, Robot-ics and Vision, Kunming, China (2004)
Warren, D.: Automated leaf shape description for variety testing in chrysanthemums. In: Proceedings of IEE 6th International Conference Image Processing and Its Applications (1997)
Brendel, T., Schwanke, J., Jensch, P., Megnet, R.: Knowledgebased object recognition for different morphological classes of plants. In: Proceedings of SPIE, vol. 2345 (1995)
Li, Y., Zhu, Q., Cao, Y., Wang, C.: A leaf vein extraction method based on snakes technique. In: Proceedings of IEEE International Conference on Neural Networks and Brain (2005)
Fu, H., Chi, Z.: Combined thresholding and neural network approach for vein pattern extraction from leaf images. IEE Proceedings-Vision, Image and Signal Processing 153(6) (2006)
Nam, Y., Hwang, E.-J., Byeon, K.: ELIS: An efficient leaf image retrieval system. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 589–597. Springer, Heidelberg (2005)
Fu, H., Chi, Z.: A two-stage approach for leaf vein extraction. In: Proceedings of IEEE International Conference on Neural Networks and Signal Processing, Nanjing, China (2003)
Wang, Z., Chi, Z., Feng, D.: Shape based leaf image retrieval. IEE Proceedings-Vision, Image and Signal Processing 150(1) (2003)
Qi, H., Yang, J.G.: Sawtooth feature extraction of leaf edge based on support vector machine. in Proceedings of the Second International Conference on Machine Learning and Cybernetics.(2003)
Hong, S.M., Simpson, B., Baranoski, G.V.G.: Interactive venationbased leaf shape modeling. Computer Animation and Virtual Worlds. 16 (2005)
Gouveia, F., Filipe, V., Reis, M., Couto, C., Bulas-Cruz, J.: Biometry: the characterisation of chestnut-tree leaves using computer vision. In: Proceedings of IEEE International Symposium on Industrial Electronics, Guimaraes, Portugal (1997)
Gu, X., Du, J.-X., Wang, X.-F.: Leaf recognition based on the combination of wavelet transform and gaussian interpolation. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 253–262. Springer, Heidelberg (2005)
Wang, X.-F., Du, J.-X., Zhang, G.-J.: Recognition of leaf images based on shape features using a hypersphere classifier. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 87–96. Springer, Heidelberg (2005)
Mallat, S.A.: Theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 31, 674–693 (1989)
Kadambe, S., Boudreaux-Bartels, G.F.: Application of the Wavelet Transform for Pitch Detection of Speech Signals. IEEE Transactions on Information Theory 32, 712–718 (1992)
Mukherjee, S., Rifkin, R.: Support Vector Machine Classification of Microarray Data, CBCL Paper 182, Artificial Intelligence Lab. Memo. 1676. MIT, Cambridge (1999)
Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Ares, M.J., Haussler, D.: Support vector machine classification of microarray gene expression data. Proc. Natl. Acad. Sci. USA 97, 262–267 (2000)
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Liu, J., Zhang, S., Deng, S. (2009). A Method of Plant Classification Based on Wavelet Transforms and Support Vector Machines. 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_29
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DOI: https://doi.org/10.1007/978-3-642-04070-2_29
Publisher Name: Springer, Berlin, Heidelberg
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