Multimedia Tools and Applications

, Volume 76, Issue 3, pp 4599–4615 | Cite as

Plant identification via multipath sparse coding

  • Heyan Zhu
  • Xinyuan Huang
  • Shengping Zhang
  • Pong C. Yuen


In this paper, we propose a novel plant identification method based on multipath sparse coding using SIFT features, which avoids the need of feature engineering and the reliance on botanical taxonomy. In particular, the proposed method uses five paths to model the shape and texture features of plant images, and at each path it learns the dictionaries with different sizes using hierarchical sparse coding. Finally, we apply the learned representation for plant identification using linear SVM for classification. We evaluate the proposed method on several plant datasets and find that multi-organ is more informative than single organ for botanist. Experimental results also validate that the proposed method outperforms the state-of-the-art methods.


Plant identification Multipath sparse coding SIFT descriptor Multi-organ Linear SVM 


  1. 1.
    Belhumeur P, Chen D, Feiner S, Jacobs D, Kress W, Ling H, Lopez I, Ramamoorthi R, Sheorey S, White S, Zhang L (2008) Searching the world’s herbaria: a system for visual identification of plant species. In: European Conference on Computer Vision (ECCV), p 116–129Google Scholar
  2. 2.
    Bo L, Ren X, Fox D (2013) Multipath sparse coding using hierarchical matching pursuit. In: CVPRGoogle Scholar
  3. 3.
    Boureau Y, Bach F, LeCun Y, Ponce J (2010) Learning mid-level features for recognition. In: CVPRGoogle Scholar
  4. 4.
    Caballero C, Aranda MC (2010) Plant species identification using leaf image retrieval. In: ACM International Conference on Image and Video Retrieval (CIVR), p 327–334Google Scholar
  5. 5.
    Du JX, Wang XF, Hang GJ (2007) Leaf shape based plant species recognition. Appl Math Comput 185(2):883–893MATHGoogle Scholar
  6. 6.
    Fiel S, Sablatnig R (2011) Automated identification of tree species from images of the bark, leaves and needles. 16th Computer Vision Winter Workshop, AustriaGoogle Scholar
  7. 7.
    Goëau H, Bonnet P, Barbe J, Bakic V, Joly A, Molino J-F (2012) Multi-organ plant identification. In: the Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data. p 41–44Google Scholar
  8. 8.
    Goëau H, Bonnet P, Joly A (2015) LifeCLEF plant identification taskGoogle Scholar
  9. 9.
    Guru DS, Sharath YH, Manjunath S (2010) Texture features and KNN in classification of flower images. IJCA Special Issue on Recent Trends in Image Processing and Pattern RecognitionGoogle Scholar
  10. 10.
    Han J, Sun L, Hu X, Han J, Shao L (2014) Spatial and temporal visual attention prediction in videos using eye movement data. Neurocomputing 145:140–153CrossRefGoogle Scholar
  11. 11.
    Han J, Wang D, Shao L, Qian X, Cheng G, Han J (2014) Image visual attention computation and application via the learning of object attributes. Mach Vis Appl 25(7):1671–1683CrossRefGoogle Scholar
  12. 12.
    Hsiao JK, Kang LW, Chang CL, Lin CY (2014) Learning sparse representation for leaf image recognition. In: ICCEGoogle Scholar
  13. 13.
    Hsiao JK, Kang LW, Chang CL, Lin CY (2014) Comparative study of leaf image recognition with a novel learning-based approach. Science and Information Conference. London, p 389–393Google Scholar
  14. 14.
    Kim S-J, Kim B-W, Kim D-P (2011) Tree recognition for landscape using by combination of features of its leaf, flower and bark. SICE Annual Conference. Tokyo, p 1147–1151Google Scholar
  15. 15.
    Kulkarni T, Uke NJ (2014) Implementation of image based flower classification system. Int J Comput Sci Bus Inform (IJCSBI) 13(1):35–44Google Scholar
  16. 16.
    Kumar N, Belhumeur PN, Biswas A, Jacobs DW, Kress WJ, Lopez IC, Soares JVB (2012) Leafsnap: a computer vision system for automatic plant species identification. ECCV, p 502–516Google Scholar
  17. 17.
    Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPRGoogle Scholar
  18. 18.
    Lee K-B, Hong K-S (2013) An implementation of leaf recognition system using leaf vein and shape. Int J Bio-Sci Bio-Technol 5:57–65CrossRefGoogle Scholar
  19. 19.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis (IJCV) 60(2):91–110CrossRefGoogle Scholar
  20. 20.
    Mabrouk AB, Najjar A, Zagrouba E (2014) Image flower recognition based on a new method for color feature extraction. International Conference on Computer Vision Theory and Application (VISAPP), p 201-206Google Scholar
  21. 21.
    Metre V, Ghorpade J (2013) An overview of the research on texture based plant leaf classification. IJCSN Int J Comput Sci Netw 2(3)Google Scholar
  22. 22.
    Mouine S, Yahiaoui I, Erroust-Blondet A, Joyeux L, Selmi S, Goëau H (2013) An android application for leaf-based plant identification. Proc. ACM Int. Conf. Multimedia RetrievalGoogle Scholar
  23. 23.
    Mouine S, Yahiaoui I, Verroust-Blondet A (2013) Advanced shape context for plant species identification using leaf image retrieval. In: Proceedings of the second ACM International Conference on Multimedia Retrieval. ACM, p 49Google Scholar
  24. 24.
    Nilsback M-E, Zisserman A. Automated flower classification over a large number of classes. In: Indian Conference on Computer Vision, Graphics and Image Processing, p 722–729Google Scholar
  25. 25.
    Pallavi P, Veena Devi VS (2014) Leaf recognition based on feature extraction and zernike moments. International Conference On Advances in Computer & Communication Engineering (ACCE), p 67–73Google Scholar
  26. 26.
    Patel HN, Dr. Jain RK, Dr. Joshi MV (2011) Fruit detection using improved multiple features based algorithm. Int J Comput Appl 13(2)Google Scholar
  27. 27.
    Peng P, Shao L, Han J, Han J (2015) Saliency-aware image-to-class distances for image classification. Neurocomputing 166:337–345CrossRefGoogle Scholar
  28. 28.
    Song Y, Glasbey CA, Horgan GW, Polder G, Dieleman JA, Van der Heijden GWAM (2014) Automatic fruit recognition and counting from multiple images. Biosyst Eng 118:203–215CrossRefGoogle Scholar
  29. 29.
  30. 30.
    Tang J, Shao L, Li X (2014) Efficient dictionary learning for visual categorization. Comput Vis Image Underst 124:91–98CrossRefGoogle Scholar
  31. 31.
    Wang Z, Chi Z, Feng D (2003) Shape based leaf image retrieval. IEE Proc Vis Image Signal Process 150(1):34–43CrossRefGoogle Scholar
  32. 32.
    Wang X, Huang D, Dua J, Xu H, Heutte L (2008) Classification of plant leaf images with complicated background. Appl Math Comput 205:916–926MathSciNetMATHGoogle Scholar
  33. 33.
    Wang Z, Lu B, Chi Z, Feng D (2011) Leaf image classification with shape context and SIFT descriptors. In: Digital image computing: techniques and applications (DICTA), p 650–654Google Scholar
  34. 34.
    Wang J, Yang J, Yu K, Lv F, Huang T, Guo Y (2010) Locality-constrained linear coding for image classification. In: CVPRGoogle Scholar
  35. 35.
    Wilf P, Zhang S, Chikkerur S, Little S, Wing S, Serre T (2016) Computer vision cracks the leaf code. PNAS, 113(12): 3305-3310Google Scholar
  36. 36.
    Xiao XY, Hu R, Zhang SW, Wang XF (2010) HOG based approach for leaf classification, vol. 6216 of Lecture Notes in Computer Science, book section 19. Springer Berlin Heidelberg, p 149–155Google Scholar
  37. 37.
    Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: CVPRGoogle Scholar
  38. 38.
    Yu K, Lin Y, Lafferty J (2011) Learning image representations from the pixel level via hierarchical sparse coding. In: CVPRGoogle Scholar
  39. 39.
    Zhang B, Ji S, Li L, Zhang S, Yang W. Sparsity analysis versus sparse representation classifier. Neuro Comput, accepted. doi:10.1016/j.neucom.2015.06.052
  40. 40.
    Zhang B, Perina A, Murino V, Del Bue A (2015) Sparse representation classification with manifold constraints transfer. CVPR, p 4557–4565Google Scholar
  41. 41.
    Zhang S, Yao H, Sun X, Lu X (2013) Sparse coding based visual tracking: Review and experimental comparison. Pattern Recogn 46(7):1772–1788CrossRefGoogle Scholar
  42. 42.
    Zhang S, Zhou H, Jiang F, Li X (2015) Robust visual tracking using structurally random projection and weighted least squares. IEEE Trans Circuits Syst Video Technol 25(11):1749–1760CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Heyan Zhu
    • 1
    • 2
  • Xinyuan Huang
    • 3
  • Shengping Zhang
    • 4
  • Pong C. Yuen
    • 5
  1. 1.School of InformationBeijing Forestry UniversityBeijingChina
  2. 2.School of Opto-Electronic InformationYantai UniversityYantaiChina
  3. 3.Institute of Animation and Digital ArtCommunication University of ChinaBeijingChina
  4. 4.School of Computer Science and TechnologyHarbin Institute of TechnologyWeihaiChina
  5. 5.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina

Personalised recommendations