A Venation-Based Leaf Image Classification Scheme

  • Jin-Kyu Park
  • EenJun Hwang
  • Yunyoung Nam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4182)


Most content-based image retrieval systems use image features such as textures, colors, and shapes. However, in the case of leaf image, it is not appropriate to rely on color or texture features only because such features are similar in most leaves. In this paper, we propose a novel leaf image retrieval scheme which first analyzes leaf venation for leaf categorization and then extracts and utilizes shape feature to find similar ones from the categorized group in the database. The venation of a leaf corresponds to the blood vessel of organisms. Leaf venations are represented using points selected by the curvature scale scope corner detection method on the venation image, and categorized by calculating the density of feature points using non-parametric estimation density. We show its effectiveness by performing several experiments on the prototype system.


Feature Point Image Retrieval Query Image Secondary Vein Leaf Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kim, S., Tak, Y., Nam, Y., Hwang, E.: mClover: mobile Content-based Leaf Image Retrieval System. In: ACM Multimedia 2005 (2005)Google Scholar
  2. 2.
    Sonobe, H., Takagi, S., Yoshimoto, F.: Mobile Computing System for Fish Image Retrieval. In: Proc. of International Workshop on Advanced Image Technology (IWAIT) (poster session), Singapore, January 2004, pp. 33–37 (2004)Google Scholar
  3. 3.
    Parzen, E.: On Estimation of a Probability Density Function and Mode. Ann. Math. Statist. 33, 1065–1076 (1962)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Sklansky, C., et al.: Minimum perimeter polygons of digitized silhouetts (1972)Google Scholar
  5. 5.
    Nam, Y., Hwang, E.: A Shape-Based Retrieval Scheme for Leaf Image. In: Ho, Y.-S., Kim, H.-J. (eds.) PCM 2005. LNCS, vol. 3767, pp. 876–887. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Im, C., Nishida, H., Kunii, T.L.: Recognizing Plant Species by Normalized Leaf Shapes. In: Vision Interface 1999, Trois-Rivières, Canada, May 19-21, pp. 397–404 (1999)Google Scholar
  7. 7.
    Wang, Z., Chi, Z., Feng, D., Wang, Q.: Leaf Image Retrieval with Shape Features. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 477–487. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Lee, C.B.: Illustrated flora of Korea. In: Hangmoonsa (1999) ISBN-8971871954Google Scholar
  9. 9.
    Mokhtarian, F., Suomela, R.: Curvature Scale Scope Based Image Corner Detection. In: Proc. European Signal Processing Conference, Greece, pp. 2549–2552 (1998)Google Scholar
  10. 10.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  11. 11.
    Alt, H., Behrends, B., Blomer, J.: Approximate matching of polygonal shapes. Ann. Math. Artif. Intell. 13, 251–266 (1995)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Wang, Z., Chi, Z., Feng, D., Wang, Q.: Leaf Image Retrieval with Shape Features. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 477–487. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Mokhtarian, F., Abbasi, S.: Matching Shapes with Self-Intersections: Application to Leaf Classification. IEEE Transactions on Image Processing 13(5), 653–661 (2004)CrossRefGoogle Scholar
  14. 14.
    The MathWorks - MATLAB and Simulink for Technical Computing,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jin-Kyu Park
    • 1
  • EenJun Hwang
    • 1
  • Yunyoung Nam
    • 2
  1. 1.School of Electrical EngineeringKorea UniversitySeoulKorea
  2. 2.Graduate School of Information and CommunicationAjou UniversitySuwon, Kyunggi-DoKorea

Personalised recommendations