Skip to main content

Fast and Effective Retrieval of Plant Leaf Shapes

  • Conference paper
Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7725))

Included in the following conference series:

Abstract

In this paper, a novel shape description and matching method based on multi-level curve segment measures (MLCSM) is proposed for plant leaf image retrieval. MLCSM extracts the statistical features of shape contour via measuring the curve bending, convexity and concavity of the curve segments with different length of shape contour to describe the shape. This method not only finely captures the global and local features, but also is very compact and has very low computational complexity. The performance of the proposed method is evaluated on the widely used Swedish leaf database and the leaf databases collected by ourselves which contains 1200 images and 100 plant leaf species. All the experiments show the superiority of our method over the state-of-the-art shape retrieval methods.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Du, J.X., Wang, X.F., Zhang, G.J.: Leaf shape based plant species recognition. Allied Mathematics and Computation 185, 883–893 (2007)

    Article  MATH  Google Scholar 

  2. Zhang, D.S., Lu, G.J.: Review of shape representation and description techniques. Pattern Regcognition 37, 1–19 (2004)

    Article  MATH  Google Scholar 

  3. Zahn, T., Roskies, R.Z.: Fourier descriptors for plane closed curves. IEEE Trans. Comput. 21, 269–281 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kauppinen, H., Seppanen, T., Pietikainen, M.: An experimental comparison of autoregressive and Fourier-based descriptors in 2-D shape classification. IEEE Trans. PAMI 17, 201–207 (1995)

    Article  Google Scholar 

  5. Mehtre, B.M., Kankanhalli, M.S., Lee, W.F.: Shape measures for content-based image retrieval: a comparison. Information Processing & Management 33, 319–337 (1997)

    Article  Google Scholar 

  6. Zhang, D.S., Lu, G.J.: A comparative study of curvature scale space and Fourier descriptors. Journal of Visual Communication and Image Representation 14, 41–60 (2003)

    Google Scholar 

  7. Zhang, D.S., Lu, G.J.: Study and evaluation of different Fourier methods for image retrieval. Image and Vision Comput. 23, 33–49 (2005)

    Article  MATH  Google Scholar 

  8. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Pattern Anal. Mach. Intell. 24, 509–522 (2002)

    Article  Google Scholar 

  9. Lin, L., Jacobs, D.: Shape classification using the inner-distance. IEEE Trans. Pattern Anal. Machine Intell. 29, 286–299 (2007)

    Article  Google Scholar 

  10. Adamek, T., O’Connor, N.E.: A multiscale representation method for nonrigid shape with a single closed contour. IEEE Trans. on Circuits and Systems for Video Technology 14, 742–743 (2004)

    Article  Google Scholar 

  11. Alajlan, N., Rube, I.E., Kamel, M.S., Freeman, G.: Shae retrieval using triangle-area representation and dynamic space warping. Pattern Recognition 40, 1911–1920 (2007)

    Article  MATH  Google Scholar 

  12. Soderkvist, O.: Computer vision classification of leaves from swedish trees. Master thesis, Linoping Univ. (2001)

    Google Scholar 

  13. Abbasi, S., Mokhtarian, F., Kittler, J.: Curvature scale space image in shape similarity retireval. Multimedia Syst. 7, 467–476 (1999)

    Article  Google Scholar 

  14. Latecki, L.J., Lakamper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1185–1190 (2000)

    Article  Google Scholar 

  15. Gong, Y.C., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative Quantization: a procrustean approach to learning binary codes for large-scale image retrieval. In: International Conference on Computer Vision and Pattern Recognition, pp. 817–824 (2011)

    Google Scholar 

  16. Weiss, Y., Torralba, A., Fergus, R.: Spectral Hashing. In: Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS), pp. 1753–1760 (2008)

    Google Scholar 

  17. Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: International Conference on Computer Vision and Pattern Recognition, pp. 3424–3431 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, B., Gao, Y. (2013). Fast and Effective Retrieval of Plant Leaf Shapes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37444-9_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37443-2

  • Online ISBN: 978-3-642-37444-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics