Advertisement

Plant species identification based on modified local discriminant projection

  • Shanwen Zhang
  • Wenzhun Huang
  • Zhen Wang
IAPR-MedPRAI

Abstract

Plant species identification based on plant leaves is important for biological science, ecological science, and agricultural digitization. Because of the complexity and variation of the plant leaves, many classical plant species identification algorithms using plant leaf images are not enough for practical application. A modified local discriminant projection (MLDP) algorithm is proposed for plant species identification. MLDP aims to extract discriminant features for plant species identification by taking class label information into account based on the property of locality preserving. The MLDP can preserve the local geometrical structure of leaves and extract the strong discriminative ability. The experimental results on the public ICL leaf image database show the effectiveness and feasibleness of the proposed method.

Keywords

Plant species identification Maximum margin criterion (MMC) Local discriminant projection (LDP) Modified LDP (MLDP) 

Notes

Acknowledgements

This work was supported by the Grants of the National Science Foundation of China (No. 61473237). It is also supported by the basic research project of natural science in Shaanxi Province under Grant Nos. 2017ZDXM-NY-088, 2016GY-141.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. 1.
    Xiao-Feng Wang DS, Du Huang J-X et al (2008) Classification of plant leaf images with complicated background. Appl Math Comput 205(2):916–926MathSciNetzbMATHGoogle Scholar
  2. 2.
    Huang DS, JiXiang D (2008) A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans Neural Netw 19(12):2099–2115CrossRefGoogle Scholar
  3. 3.
    Du JX, Zhai CM, Wang QP (2013) Recognition of plant leaf image based on fractal dimension feature. Neurocomputing 116:150–156CrossRefGoogle Scholar
  4. 4.
    Zhang S, Lei Y-K, Dong T et al (2013) Label propagation based supervised locality projection analysis for plant leaf classification. Pattern Recogn 46(7):1891–1897CrossRefGoogle Scholar
  5. 5.
    Wang XF, Huang DS (2009) A novel density-based clustering framework by using level set method. IEEE Trans Knowl Data Eng 21(11):1515–1531CrossRefGoogle Scholar
  6. 6.
    Chaki J, Parekh R (2011) Plant leaf recognition using shape based features and neural network classifiers. Int J Adv Comput Sci Appl 2(10):41–47Google Scholar
  7. 7.
    Munisami T, Ramsurmn M, Kishnah S, Pudaruthb S et al (2015) Plant leaf recognition using shape features and colour histogram with K-nearest neighbor classifiers. Procedia Comput Sci 58:740–747CrossRefGoogle Scholar
  8. 8.
    Lu CY, Huang DS (2013) Optimized projections for sparse representation based classification. Neurocomputing 113(10):213–219CrossRefGoogle Scholar
  9. 9.
    Lu C, Feng J, Lin Z et al (2018) Subspace clustering by block diagonal representation. IEEE Trans Pattern Anal Mach Intell 99:1–11Google Scholar
  10. 10.
    Yang LW, Wang XF (2012) Leaf image recognition using Fourier transform based on ordered sequence. Intell Comput Technol Lect Notes Comput Sci 7389:393–400Google Scholar
  11. 11.
    Zhang S, Lei Y-K (2011) Modified locally linear discriminant embedding for plant leaf recognition. Neurocomputing 74:2284–2290CrossRefGoogle Scholar
  12. 12.
    Zhang S, Lei Y-K, Yan-Hua W (2011) Semi-supervised locally discriminant projection for recognition. Knowl Based Syst 24:341–346CrossRefGoogle Scholar
  13. 13.
    Yan S, Xu D, Zhang B, Zhang H-J (2005) Graph embedding: a general framework for dimensionality reduction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 830–837Google Scholar
  14. 14.
    Yan S, Xu D, Zhang B, Zhang HJ (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRefGoogle Scholar
  15. 15.
    Chen HT, Chang HW, Liu TL (2005) Local discriminant embedding and its variants. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 846–853Google Scholar
  16. 16.
    Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17:157–165CrossRefGoogle Scholar
  17. 17.
    Yu L, Xie J, Zhu L (2011) A local discriminant projection method based on objective space. J Electron Inf Technol 33(10):2390–2395CrossRefGoogle Scholar
  18. 18.
    Belhumeur V, Hespanha J, Kriegman D (1997) Eigenfaces vs fisher faces: recognition using class specific linear projection. IEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  19. 19.
    Shanwen Z, Xianfeng W, Zhen W et al (2015) Probability locality preserving discriminant projections for plant recognition. Trans Chin Soc Agric Eng (Trans CSAE) 31(11):215–220 (in Chinese with English abstract) Google Scholar
  20. 20.
    Loog M, Duin RPW, Haebumbach R (2001) Multiclass linear dimension reduction by weighted pairwise fisher criteria. IEEE Trans Pattern Anal Mach Intell 23(7):762–766CrossRefGoogle Scholar
  21. 21.
    Li B, Wang C, Huang DS (2009) Supervised feature extraction based on orthogonal discriminant projection. Neurocomputing 73:191–196CrossRefGoogle Scholar
  22. 22.
    Hu R, Jia W, Ling H et al (2012) Multiscale distance matrix for fast plant leaf recognition. IEEE Trans Image Process 21(11):4667–4672MathSciNetCrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Information EngineeringXijing UniversityXi’anChina

Personalised recommendations