International Conference on Computer Analysis of Images and Patterns

CAIP 2015: Computer Analysis of Images and Patterns pp 326-335 | Cite as

Leaf-Based Plant Identification Through Morphological Characterization in Digital Images

  • Arturo Oncevay-Marcos
  • Ronald Juarez-Chambi
  • Sofía Khlebnikov-Núñez
  • César Beltrán-Castañón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9257)

Abstract

The plant species identification is a manual process performed mainly by botanical scientists based on their experience. In order to improve this task, several plant classification processes has been proposed applying pattern recognition. In this work, we propose a method combining three visual attributes of leaves: boundary shape, texture and color. Complex networks and multi-scale fractal dimension techniques were used to characterize the leaf boundary shape, the Haralick’s descriptors for texture were extracted, and color moments were calculated. Experiments were performed on the ImageCLEF 2012 train dataset, scan pictures only. We reached up to 90.41% of accuracy regarding the leaf-based plant identification problem for 115 species.

Keywords

Leaf-based plant identification Complex networks Multi-scale fractal dimension Haralick’s descriptors Color moments 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bruno, O.M., de Oliveira Plotze, R., Falvo, M., de Castro, M.: Fractal dimension applied to plant identification. Inf. Sci. 178(12), 2722–2733 (2008)CrossRefGoogle Scholar
  2. 2.
    Lee, C.L., Chen, S.Y.: Classification of leaf images. Int. J. Imaging Syst. Technol. 16(1), 15–23 (2006)CrossRefGoogle Scholar
  3. 3.
    Pauwels, E.J., de Zeeuw, P.M., Ranguelova, E.B.: Computer-assisted tree taxonomy by automated image recognition. Eng. Appl. Artif. Intell. 22(1), 26–31 (2009)CrossRefGoogle Scholar
  4. 4.
    Gu, X., Du, J.-X., Wang, X.-F.: Leaf recognition based on the combination of wavelet transform and gaussian interpolation. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 253–262. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  5. 5.
    Backes, A.R., Bruno, O.M.: Shape classification using complex network and multi-scale fractal dimension. Pattern Recogn. Lett. 31(1), 44–51 (2010)CrossRefGoogle Scholar
  6. 6.
    Casanova, D., Florindo, J.B., Gonçalves, W.N., Bruno, O.M.: Ifsc/usp at imageclef 2012: plant identification task. In: Proceeding of CLEF 2012 Labs and Workshop, Notebook Papers (2012)Google Scholar
  7. 7.
    Arora, A., Gupta, A., Bagmar, N., Mishra, S., Bhattacharya, A.: A plant identification system using shape and morphological features on segmented leaflets: team iitk, clef 2012. In: Proceeding of CLEF 2012 Labs and Workshop, Notebook Papers (2012)Google Scholar
  8. 8.
    de Oliveira Plotze, R., Falvo, M., Pádua, J.G., Bernacci, L.C., Vieira, M.L.C., Oliveira, G.C.X., Bruno, O.M.: Leaf shape analysis using the multiscale minkowski fractal dimension, a new morphometric method: a study with passiflora (passifloraceae). Can. J. Bot. 83(3), 287–301 (2005)CrossRefGoogle Scholar
  9. 9.
    Zhang, X., Zhang, F.: Images features extraction of tobacco leaves. In: Congress on Image and Signal Processing, CISP 2008, vol. 2, 773–776. IEEE (2008)Google Scholar
  10. 10.
    Man, Q.K., Zheng, C.H., Wang, X.F., Lin, F.Y.: Recognition of plant leaves using support vector machine. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, F.-Y. (eds.) ICIC 2008. CCIS, vol. 15, pp. 192–199. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  11. 11.
    Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: Leaf classification using shape, color, and texture features. arXiv preprint arXiv:1401.4447 (2013)
  12. 12.
    Choras, R.S.: Image feature extraction techniques and their applications for cbir and biometrics systems. International Journal of Biology and Biomedical Engineering 1(1), 6–16 (2007)Google Scholar
  13. 13.
    Kebapci, H., Yanikoglu, B., Unal, G.: Plant image retrieval using color, shape and texture features. The Computer Journal (2010) bxq037Google Scholar
  14. 14.
    Lin, F.Y., Zheng, C.H., Wang, X.F., Man, Q.K.: Multiple classification of plant leaves based on gabor transform and lBP operator. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. CCIS, vol. 15, pp. 432–439. Springer, Heildelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Ehsanirad, A.: Plant classification based on leaf recognition. International Journal of Computer Science and Information Security 8(4), 78–81 (2010)Google Scholar
  16. 16.
    Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: Neural network application on foliage plant identification. arXiv preprint arXiv:1311.5829 (2013)
  17. 17.
    Goëau, H., Bonnet, P., Joly, A., Barthelemy, D., Boujemaa, N., Molino, J.: The imageclef 2012 plant image identification task. In: ImageCLEF 2012 Working Notes (2012)Google Scholar
  18. 18.
    Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)Google Scholar
  19. 19.
    Werbos, P.: Beyond regression: New tools for prediction and analysis in the behavioral sciences. (1974)Google Scholar
  20. 20.
    Backes, A.R., Casanova, D., Martinez, O.B.: A complex network-based approach for boundary shape analysis. Pattern Recogn. 42, 54–67 (2009)MATHCrossRefGoogle Scholar
  21. 21.
    Barabási, A.L.: Linked: The new science of networks. (2002)Google Scholar
  22. 22.
    Castañón, C.A.B., Chambi, R.J.: Using complex networks for offline handwritten signature characterization. In: Bayro-Corrochano, E., Hancock, E. (eds.) CIARP 2014. LNCS, vol. 8827, pp. 580–587. Springer, Heidelberg (2014) Google Scholar
  23. 23.
    Backes, A.R., Martinez, O.: Fractal and multi-scale fractal dimension analysis: a comparative study of bouligand-minkowski method. CoRR abs/1201.3153 (2012)Google Scholar
  24. 24.
    Shih, J.-L., Chen, L.-H.: Color image retrieval based on primitives of color moments. In: Chang, S.-K., Chen, Z., Lee, S.-Y. (eds.) VISUAL 2002. LNCS, vol. 2314, p. 88. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  25. 25.
    Stricker, M.A., Orengo, M.: Similarity of color images. In: IS&T/SPIE’s Symposium on Electronic Imaging: Science & Technology, International Society for Optics and Photonics, pp. 381–392 (1995)Google Scholar
  26. 26.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 6, 610–621 (1973)CrossRefGoogle Scholar
  27. 27.
    Gonzalez, R.C., Woods, R.E.: Digital image processing (2002)Google Scholar
  28. 28.
    Porebski, A., Vandenbroucke, N., Macaire, L.: Neighborhood and haralick feature extraction for color texture analysis. In: Conference on Colour in Graphics, Imaging, and Vision, Society for Imaging Science and Technology, vol. 2008, pp. 316–321 (2008)Google Scholar
  29. 29.
    Brilhador, A., Colonhezi, T.P., Bugatti, P.H., Lopes, F.M.: Combining texture and shape descriptors for bioimages classification: a case of study in imageCLEF dataset. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part I. LNCS, vol. 8258, pp. 431–438. Springer, Heidelberg (2013) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arturo Oncevay-Marcos
    • 1
  • Ronald Juarez-Chambi
    • 1
  • Sofía Khlebnikov-Núñez
    • 1
  • César Beltrán-Castañón
    • 1
  1. 1.Department of Engineering, Research Group on Pattern Recognition and Applied Artificial IntelligencePontificia Universidad Católica Del PerúLimaPerú

Personalised recommendations