Plant Leaf Recognition Using Ridge Filter and Curvelet Transform with Neuro-Fuzzy Classifier

  • Jyotismita Chaki
  • Ranjan Parekh
  • Samar Bhattacharya
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)

Abstract

The current work proposes an innovative methodology for the recognition of plant species by using a combination of shape and texture features from leaf images. The leaf shape is modeled using Curvelet Coefficients and Invariant Moments while texture is modeled using a Ridge Filter and some statistical measures derived from the filtered image. As the features are sensitive to geometric orientations of the leaf image, a pre processing step is performed to make features invariant to geometric trans-formations. To classify images to pre-defined classes, a Neuro fuzzy classifier is used. Experimental results show that the method achieves acceptable recognition rates for images varying in texture, shape and orientation.

Keywords

Curvelet transform Invariant moment Ridge filter Neuro fuzzy 

References

  1. 1.
    Beghin, T., Cope, J.S., Remagnino, P., Barman, S.: Shape and texture based plant leaf classification. In: International Conference on Advanced Concepts for Intelligent Vision Systems (ACVIS), pp. 345–353 (2010)Google Scholar
  2. 2.
    Kebapci, H., et al.: Plant image retrieval using color, shape and texture features. Comput. J. 53(1), 1–16 (2010)CrossRefGoogle Scholar
  3. 3.
    Du, J.-X., Zhai, C.-M., Wang, Q.-P.: Recognition of plant leaf image based on fractal dimension feature. Neurocomputing 116, 150–156 (2013)CrossRefGoogle Scholar
  4. 4.
    Yang, L.W., Wang, X.F.: Leaf image recognition using fourier transform based on ordered sequence. Springer LNCS. 7389, 393–400 (2012)Google Scholar
  5. 5.
    Wang, Q-P., Du, J-X., Zhai, C-M.: Recognition of leaf image based on ring projection wavelet fractal feature. In: International Journal Innovative Computing, Information and Control, pp. 240–246 (2012)Google Scholar
  6. 6.
    Candès, E., Donoho, D.: Curvelets—a Surprisingly Effective Nonadaptive Representation for Objects with Edges, pp. 1–10. Vanderbilt University Press, Nashville (2000)Google Scholar
  7. 7.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)MATHCrossRefGoogle Scholar
  8. 8.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. PAMI 20(8), 777–789 (1998)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Kadir, A., Nogroho, L.E., Susanto, A., Santosa, P.I.: Neural network application on foliage plant identification. Int. J. Comput. Appl. 29, 15–22 (2011)Google Scholar
  11. 11.
    Wang, X., Liang, J., Guo, F.: Feature extraction algorithm based on dual-scale decomposition and local binary descriptors for plant leaf recognition. Elsevier Digital Image Process. 34, 101–107 (2014)CrossRefGoogle Scholar
  12. 12.
    Caglayan, A., Oguzhan, G., Can, A.B.: A plant recognition approach using shape and color features in leaf images, vol. 8157, pp. 161–170 Springer LNCS (2013)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Jyotismita Chaki
    • 1
  • Ranjan Parekh
    • 1
  • Samar Bhattacharya
    • 2
  1. 1.School of Education TechnologyJadavpur UniversityKolkataIndia
  2. 2.Department of Electrical EngineeringJadavpur UniversityKolkataIndia

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