A Fast Leaf Recognition Algorithm based on SVM Classifier and High Dimensional Feature Vector

Cecilia Di Ruberto, Lorenzo Putzu

Abstract

Plants are fundamental for human beings, so it's very important to catalog and preserve all the plants species. Identifying an unknown plant species is not a simple task. Automatic image processing techniques based on leaves recognition can help to find the best features useful for plant representation and classification. Many methods present in literature use only a small and complex set of features, often extracted from the binary images or the boundary of the leaf. In this work we propose a leaf recognition method which uses a new features set that incorporates shape, color and texture features. A total of 138 features are extracted and used for training of a SVM model. The method has been tested on Flavia dataset, showing excellent performance both in terms of accuracy that often reaches 100\%, and in terms of speed, less than a second to process and extract features from an image.

References

  1. Arribas, J. I., Snchez-Ferrero, G. V., Ruiz-Ruiz, G., and Gmez-Gil, J. (2011). Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture, 78(1):9 - 18.
  2. Chaki, J. and Parekh, R. (2011). Plant leaf recognition using shape based features and neural network classifiers. International Journal of Advanced Computer Science and Applications, 2(10):41 - 47.
  3. Cheng, S.-C., Jhou, J.-J., and Liou, B.-H. (2007). Pda plant search system based on the characteristics of leaves using fuzzy function. In New Trends in Applied Artificial Intelligence, volume 4570 of Lecture Notes in Computer Science, pages 834-844. Springer.
  4. Du, J.-X., Huang, D.-S., Wang, X.-F., and Gu, X. (2006). Computer-aided plant species identification based on leaf shape matching technique. Transactions of the Institute of Measurement and Control, 28(3):275-285.
  5. Du, J.-X., Wang, X.-F., and Zhang, G.-J. (2007). Leaf shape based plant species recognition. Applied Mathematics and Computation, 185(2):883 - 893.
  6. Ehsanirad, A. (2010). Plant classification based on leaf recognition. International Journal of Computer Science and Information Security, 8(4):78-81.
  7. Gao, L., Lin, X., Zhao, W., Chen, S., and Huang, H. (2010a). An algorithm of excising leafstalk while keeping its main body intact for leaf recognition. In Image and Signal Processing (CISP), 2010 3rd International Congress on, volume 6, pages 2732-2736.
  8. Gao, L., Lin, X., Zhong, M., and Zeng, J. (2010b). A neural network classifier based on prior evolution and iterative approximation used for leaf recognition. In Natural Computation (ICNC), 2010 Sixth International Conference on, volume 2, pages 1038-1043.
  9. Gonzalez, R. C., Woods, R. E., and Eddins, S. L. (2004). Digital Image Processing Using MATLAB. Pearson Prentice Hall Pearson Education, New Jersey, USA, 1st edition.
  10. Haralick, R., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, SMC3(6):610-621.
  11. Hu, M.-K. (1962). Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, 8(2):179-187.
  12. Im, C., Nishida, H., and Kunii, T. (1998). Recognizing plant species by leaf shapes-a case study of the acer family. In 14th International Conference on Pattern Recognition, volume 2, pages 1171-1173.
  13. Kadir, A., Nugroho, L. E., Susanto, A., and Santosa, P. I. (2011). A comparative experiment of several shape methods in recognizing plants. International Journal of Computer Science & Information Technology, 3(3):256-263.
  14. Kulkarni, A. H., Rai, D. H. M., Jahagirdar, D. K. A., and Upparamani, P. S. (2013). A leaf recognition technique for plant classification using rbpnn and zernike moments. International Journal of Advanced Research in Computer and Communication Engineering, 2(1):82-93.
  15. Lee, C.-L. and Chen, S.-Y. (2006). Classification of leaf images. International Journal of Imaging Systems and Technology, 16(1):15-23.
  16. Lee, K. B. and Hong, K. S. (2013). An implementation of leaf recognition system using leaf vein and shape. International Journal of Bio-Science and BioTechnology, 5(2):57-65.
  17. Lin, H. and Peng, H. (2008). Machine recognition for broad-leaved trees based on synthetic features of leaves using probabilistic neural network. In Computer Science and Software Engineering, 2008 International Conference on, volume 4, pages 871-877.
  18. Machado, B. B., Casanova, D., Gonalves, W. N., and Bruno, O. M. (2013). Partial differential equations and fractal analysis to plant leaf identification. Journal of Physics: Conference Series, 410(1).
  19. Man, Q.-K., Zheng, C.-H., Wang, X.-F., and Lin, F.-Y. (2008). Recognition of plant leaves using support vector machine. In Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, volume 15 of Communications in Computer and Information Science, pages 192-199. Springer Berlin Heidelberg.
  20. Pauwels, E. J., de Zeeuw, P. M., and Ranguelova, E. (2009). Computer-assisted tree taxonomy by automated image recognition. Engineering Applications of Artificial Intelligence, 22(1):26-31.
  21. Singh, K., Gupta, I., and Gupta, S. (2010). Svm-bdt pnn and fourier moment technique for classification of leaf shape. International Journal of Signal Processing, Image Processing and Pattern Recognition, 3(4):67-78.
  22. Valliammal, N. and Geethalakshmi, S. N. (2011). Hybrid image segmentation algorithm for leaf recognition and characterization. In International Conference on Process Automation, Control and Computing, pages 1-6.
  23. Wang, Z., Chi, Z., and Feng, D. (2003). Shape based leaf image retrieval. Vision, Image and Signal Processing, IEEE Proceedings, 150(1):34-43.
  24. Wu, Q., Zhou, C., and Wang, C. (2006). Feature extraction and automatic recognition of plant leaf using artificial neural network. Avances en Ciencias de la Computacion, pages 5-16.
  25. Wu, S., Bao, F., Xu, E., Wang, Y.-X., Chang, Y.-F., and Xiang, Q.-L. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. In IEEE International Symposium on Signal Processing and Information Technology, pages 11-16.
  26. Zhang, S. and Lei, Y.-K. (2011). Modified locally linear discriminant embedding for plant leaf recognition. Neurocomputing, 74(1415):2284 - 2290.
  27. Zulkifli, Z., Saad, P., and Mohtar, I. (2011). Plant leaf identification using moment invariants amp; general regression neural network. In 11th International Conference on Hybrid Intelligent Systems (HIS), pages 430-435.
Download


Paper Citation


in Harvard Style

Di Ruberto C. and Putzu L. (2014). A Fast Leaf Recognition Algorithm based on SVM Classifier and High Dimensional Feature Vector . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 601-609. DOI: 10.5220/0004740606010609


in Bibtex Style

@conference{visapp14,
author={Cecilia Di Ruberto and Lorenzo Putzu},
title={A Fast Leaf Recognition Algorithm based on SVM Classifier and High Dimensional Feature Vector},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={601-609},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004740606010609},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - A Fast Leaf Recognition Algorithm based on SVM Classifier and High Dimensional Feature Vector
SN - 978-989-758-003-1
AU - Di Ruberto C.
AU - Putzu L.
PY - 2014
SP - 601
EP - 609
DO - 10.5220/0004740606010609