Abstract
Recognizing plant leaves has so far been an important and difficult task. This paper introduces a method of recognizing leaf images based on shape features using a hypersphere classifier. Firstly, we apply image segmentation to the leaf images. Then we extract eight geometric features including rectangularity, circularity, eccentricity, etc, and seven moment invariants for classification. Finally we propose using a moving center hypersphere classifier to address these shape features. As a result there are more than 20 classes of plant leaves successfully classified. The average correct recognition rate is up to 92.2 percent.
This work was supported by the NSF of China (Nos.60472111 and 60405002).
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References
Sonka, M., Hlavac, V., Boyle, R.: Image Processing,Analysis and Machine Vision, 2nd edn. Posts & Telecom Press, Beijing (2002)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)
Bian, Z., Zhang, X.: Pattern Recognition. TsingHua University Press, Beijing (1999)
Im, C., Nishida, H., Kunii, T.L.: Recognizing Plant Species by Leaf Shapes-a Case Study of the Acer Family. Proc. Pattern Recognition 2, 1171–1173 (1998)
Wang, Z., Chi, Z., Feng, D.: Fuzzy Integral for Leaf Image Retrieval. Proc. Fuzzy Systems 1, 372–377 (2002)
Saitoh, T., Kaneko, T.: Automatic Recognition of Wild Flowers. Proc. Pattern Recognition 2, 507–510 (2000)
Ridler, T.W., Calvard, S.: Picture Thresholding Using An Iterative Selection Method. IEEE Transaction on System, Man and Cybernetics 8(8), 630–632 (1978)
Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)
Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Transaction Information Theory 8(2), 179–187 (1962)
Chen, C.-C.: Improved Moment Invariants for Shape Discrimination. Pattern Recognition 26(5), 683–686 (1993)
Hart, P.E.: The Condensed Nearest Neighbor Rule. IEEE Transactions on Information Theory 14(3), 515–516 (1967)
Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)
Huang, D.S., Ip Horace, H.S., Law Ken, C.K., Chi, Z.: Zeroing Polynomials Using Modified Constrained Neural Network Approach. IEEE Trans. On Neural Networks 16(3), 721–732 (2005)
Huang, D.S., Ip Horace, H.S., Chi, Z.: A Neural Root Finder of Polynomials Based on Root Moments. Neural Computation 16(8), 1721–1762 (2004)
Huang, D.S.: Application of Generalized Radial Basis Function Networks to Recognition of Radar Targets. International Journal of Pattern Recognition and Artificial Intelligence 13(6), 945–962 (1999)
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Wang, XF., Du, JX., Zhang, GJ. (2005). Recognition of Leaf Images Based on Shape Features Using a Hypersphere Classifier. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_10
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DOI: https://doi.org/10.1007/11538059_10
Publisher Name: Springer, Berlin, Heidelberg
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