Abstract
Leaves from plants are proved to be a feasible source of information used to identify plant species [1]. In this paper, we present a method to recognize plant leaves employing Histograms of Oriented Gradients (HOG) as the feature descriptor. For better robustness to illumination, shadow, quality degradation, etc., five vital factors of original HOG algorithm are discussed to evaluate the respective effects in different configurations. Experimental results show that this method achieves excellent performance in recognition rate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Guyer, D.E., Miles, G.E., Schreiber, M.M., Mitchell, O.R., Vanderbilt, V.C.: Machine vision and image processing for plant identification. Trans. ASAE 29(6), 1500–1507 (1986)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005 (2005)
Albiol, A., Monzo, D., Martin, A., Sastre, J., Albiol, A.: Face recognition using HOG-EBGM. Pattern Recognition Lett. 29(10), 1537–1543 (2008)
Bertozzi, M., Broggi, A., Rose, M.D., Felisa, M., Rakotomamonjy, A., Suard, F.: A pedestrian detector using histograms of oriented gradients and a support vector machine classifier. In: Proc. Intelligent Transportation Systems Conf., pp. 143–148 (2007)
Li, B., Huang, D.S.: Locally linear discriminant embedding: An efficient method for face recognition. Pattern Recognition 41(12), 3813–3821 (2008)
Zhao, Z.-Q., Huang, D.S., Sun, B.-Y.: Human face recognition based on multiple features using neural networks committee. Pattern Recognition Letters 25(12), 1351–1358 (2004)
Huang, D.S.: Radial basis probabilistic neural networks: Model and application. Int. Journal of Pattern Recognit., and Artificial Intell. 13(7), 1083–1101 (1999)
Huang, D.S., Du, J.-X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Networks 19(12), 2099–2115 (2008)
Manh, A.G., Rabatel, G.: In-Field Classification of Weed Leaves by Machine Vision Using Deformable Templates. In: 3rd European Conference Precision Agriculture, ECPA 2001, Montpellier, June 18-28, pp. 599–604 (2001)
Wang, X.F., Huang, D.S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recognition 43(3), 603–618 (2010)
Ling, H., Jacobs, D.: Using the Inner Distance for Classification of Articulated Shapes. In: IEEE International Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 719–726 (2005)
Du, J.-X., Wang, X.-F., Huang, D.S.: Automatic plant leaves recognition system based on image processing techniques, Technical Report, Institute of Intelligent Machines, Chinese Academy of Sciences (October 2004)
ICL database of leaf images, http://www.intelengine.cn/dataset/index.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xia, Q., Zhu, HD., Gan, Y., Shang, L. (2014). Plant Leaf Recognition Using Histograms of Oriented Gradients. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-09339-0_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09338-3
Online ISBN: 978-3-319-09339-0
eBook Packages: Computer ScienceComputer Science (R0)