Skip to main content
Springer Nature Link
Log in

Supervised Locally Linear Embedding for Plant Leaf Image Feature Extraction

  • Conference paper
Emerging Intelligent Computing Technology and Applications (ICIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5754))

Included in the following conference series:

  • 1670 Accesses

Abstract

The objects of traditional plant identification were too broad and the classification features of it were usually not synthetic and the recognition rate was always slightly low. This paper gives one recognition approach based on supervised locally linear embedding (LLE) and K-nearest neighbors. The recognition results for thirty kinds of broad-leaved trees were realized and the average correct recognition rate reached 98.3%. Comparison with other recognition method demonstrated the proposed method is effective in advancing the recognition rate.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Yonekawa, S., Sakai, N., Kitani, O.: Identification of Idealized Leaf Types Using Simple Dimensionless Shape Factors by Image Analysis. Transaction of the ASAE 39(4), 1525–1533 (1996)

    Google Scholar 

  2. Abbasi, S., Mokhtarian, F., Kittler, J.: Reliable Classification of Chrysanthemum Leaves through Curvature Scale Space. In: Proceeding of International Conference on Scale-Space Theory in Computer Vision, pp. 284–295 (1997)

    Google Scholar 

  3. Mokhtarian, F., Abbasi, S.: Matching Shapes with Self-Intersection: Application to Leaf Classification. IEEE Transaction on Image Processing 13(5), 653–661 (2004)

    Article  Google Scholar 

  4. Li, Y.F., Zhu, Q.S., Cao, Y.K., Wang, C.L.: A Leaf Vein Extraction Method Based On Snakes Technique. In: Proceedings of IEEE International Conference on Neural Networks and Brain, pp. 885–888 (2005)

    Google Scholar 

  5. Du, J.X., Huang, D.S., Wang, X.F., Gu, X.: Shape Recognition Based on Neural Networks Trained by Differential Evolution Algorithm. Neurocomputing 70(4), 896–903 (2007)

    Google Scholar 

  6. Du, J.X., Huang, D.S., Gu, X.: A Novel Full Structure Optimization Algorithm for Radial Basis Probabilistic Neural Networks. Neurocomputing 70(1), 592–596 (2006)

    Article  Google Scholar 

  7. Du, J.X., Huang, D.S., Wang, X.F., Gu, X.: Shape Recognition Based on Radial Basis Probabilistic Neural Network and Application to Plant Species Identification. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 281–285. Springer, Heidelberg (2005)

    Google Scholar 

  8. Wan, Y.Y., Du, J.X., Huang, D.S.: Bark Texture Feature Extraction Based on Statistical Texture Analysis. In: Proceedings of The 2004 International Symposium on Intelligent Multimedia, Video & Speech Processing (ISIMP 2004), Hong Kong, China, pp. 482–485 (2004)

    Google Scholar 

  9. Du, J.X., Huang, D.S., Wang, X.F., Gu, X.: Computer-aided plant species identification (capsi) based on leaf shape matching technique. Transactions of the Institute of Measurement and Control 28 (2006)

    Google Scholar 

  10. Saitoh, T.K.: Takeshi.: Automatic recognition of wild flowers. In: Proceedings of 15th International Conference on Pattern Recognition (ICPR 2000), vol. 2 (2000)

    Google Scholar 

  11. Heymans, B.C., Onema, J.P., Kuti, J.O.: A neural network for opuntia leaf-form recognition. In: Proceedings of IEEE International Joint Conference on Neural Networks (1991)

    Google Scholar 

  12. Brendel, T., Schwanke, J., Jensch, P., Megnet, R.: Knowledge based object recognition for different morphological classes of plants. In: Proceedings of SPIE, vol. 2345 (1995)

    Google Scholar 

  13. Gouveia, F., Filipe, V., Reis, M., Couto, C., Bulas-Cruz, J.: Biometry: the characterization of chestnut-tree leaves using computer vision. In: Proceedings of IEEE International Symposium on Industrial Electronics, Guimaraes, Portugal (1997)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. Ridder, D., De, K.O., Okun, O., Pietikainen, M., Duin, R.P.W.: Supervised locally linear embedding. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 333–344. Springer, Heidelberg (2003)

    Google Scholar 

  16. Wang, S.L., Wang, J., Chen, H.W., et al.: SVM-based tumor classification with gene expression data. In: Li, X., Zaïane, O.R., Li, Z.-h. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 864–870. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1-3), 389–422 (2002)

    Article  MATH  Google Scholar 

  18. Vapnik, V.N.: Statistical learning theory. Wiley Inter science, New York (1998)

    MATH  Google Scholar 

  19. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Science Institute, Air-Force Engineering University, Xi’an, 710051, China

    Youqian Feng

  2. Intelligent Computation Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui, 230031, P.R. China

    Shanwen Zhang

Authors
  1. Youqian Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shanwen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, Nam-Ku, 680 - 749, Ulsan, Korea

    Kang-Hyun Jo

  3. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Hong-Hee Lee

  4. School of Electrical Engineering, University of Ulsan, South Korea

    Hee-Jun Kang

  5. e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Str. Prov. per Casamassima Km., 3-70010, Valenzano, (BA), Italy

    Vitoantonio Bevilacqua

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Feng, Y., Zhang, S. (2009). Supervised Locally Linear Embedding for Plant Leaf Image Feature Extraction. 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_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04070-2_1

  • 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)

Publish with us

Policies and ethics