Skip to main content
SpringerLink
Log in

Leaf Classification Using Convexity Moments of Polygons

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

Included in the following conference series:

  • 1787 Accesses

Abstract

Research has shown that shape features can be used in the process of object recognition with promising results. However, due to a wide variety of shape descriptors, selecting the right one remains a difficult task. This paper presents a new shape recognition feature: Convexity Moment of Polygons. The Convexity Moments of Polygons is derived from the Convexity measure of polygons. A series of experimentations based on FLAVIA images dataset was performed to demonstrate the accuracy of the proposed feature compared to the Convexity measure of polygons in the field of leaf classification. A classification rate of 92% was obtained with the Convexity Moment of Polygons, 80% with the convexity Measure of Polygons using the Radial Basis function neural networks classifier (RBF).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zunic, J., Rosin, P.: A new convexity measure for polygons. IEEE Trans. Pattern Anal. Mach. Intell. 26, 923–934 (2004)

    Article  Google Scholar 

  2. Rahtu, E., Salo, M., Heikkila, J.: A new convexity measure based on a probabilistic interpretation of images. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1501–1512 (2006). doi:10.1109/TPAMI.2006.175

    Article  Google Scholar 

  3. Guo-Jun, Z., Feng, W.X., De-shuang, H.: A hypersphere method for plant leaves classification. In: Proceedings of the International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 165–168 (2004)

    Google Scholar 

  4. Ji-Xiang, D., Xiao-Feng, W., Guo-Jun, Z.: Leaf shape based plant species recognition. Appl. Math. Comput. 185(2), 883–893 (2007)

    MATH  Google Scholar 

  5. Lee, K.B., Hong, K.S.: Advanced leaf recognition based on leaf contour and centroid for plant classification. In: The 2012 International Conference on Information Science and Technology, pp. 133–135 (2012)

    Google Scholar 

  6. Cerutti, G., Tougne, L., Coquin, D., Vacavant, A.: Curvature-scale-based contour understanding for leaf margin shape recognition and species identification. In: 2013 International Conference on Computer Vision Theory and Applications, VISAP, pp. 277–284 (2013)

    Google Scholar 

  7. Hussein, A.N., Mashohor, S., Saripan, M.I.: A texture-based approach for content based image retrieval system for plant leaves images. In: 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (CSPA), pp. 11–14. IEEE, March 2011

    Google Scholar 

  8. Neto, J.C., Meyer, G.E., Jones, D.D., Samal, A.K.: Plant species identification using Elliptic Fourier leaf shape analysis. Comput. Electron. Agric. 50(2), 121–134 (2006)

    Article  Google Scholar 

  9. Stephen, G., Forrest, S., Eric, Y., Yu-Xuan, W., Yi-Fang, C., Qiao-Liang, X.: A leaf recognition algorithm for plant classification using probalistic neural network. In: Computer Science and Artificial Intelligency (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Artificial Intelligence Research, CSIR Meraka, Pretoria, South Africa

    J. R. Kala & D. Moodley

  2. University of Kwazulu-Nata, Durban, South Africa

    J. R. Kala & S. Viriri

  3. University of Cape Town, Cape Town, South Africa

    D. Moodley

Authors
  1. J. R. Kala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Viriri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Moodley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. R. Kala .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Kala, J.R., Viriri, S., Moodley, D. (2016). Leaf Classification Using Convexity Moments of Polygons. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50832-0_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation