Abstract
Plant plays an important role in human life, so it is necessary to build an automatic system for recognizing plant. Leaf classification has become a research focus for twenty years. In this paper, we propose a single connected layer (SCL) structure adding into the convolutional neural network (CNN). We use this CNN model for plant leaf identification and report the promising results on ICL leaf database. Moreover, we propose some improvement on it to let it perform better. The result shows that our advanced SCL can effectively improve the accuracy of CNN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guyer, D.E., Miles, G.E., Gaultney, L.D., et al.: Applocation of machine vision to shape analysis in leaf and plant identification. Trans. ASABE 36(1), 163–171 (1993)
LeCun, Y.: Generalization and network design strategies. Connections Perspect. North-Holland, Amsterdam, 143–155 (1989)
LeCun, Y., Jackel, L.D., Bottou, L., et al.: Learning algorithms for classification: a comparison on handwritten digit recognition. Neural networks Stat. Mech. Perspect. 261, 276 (1995)
Hinton, G.E., Salakhutdinov, R.R.: Reducing thr dimensionality of data with neural network. Science 313(5786), 504–507 (2006)
Strom, N.: Sparse connection and pruning in large dynamic artificial neural networks. In: EUROSPEECH (1997)
Won, Y., Gader, P.D., Coffield, P.C.: Morphological shared-weight networks with applications to automatic target recognition. IEEE Trans. Neural Networks 8(5), 1195–1203 (1997)
Guyon, I., Albrecht, P., LeCun, Y., et al.: Design of a neural network character recognizer for a touch terminal. Pattern Recogn. 24(2), 105–119 (1991)
Hinton, G.E., et al.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv preprint arXiv:1207.0580
Hinton, G.E., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996). (in Chinese)
Huang, D.S., Jiang, W.: A general CPL-AdS methodology for fixing dynamic parameters in dual environments. IEEE Trans. Syst. Man Cybern. Part B. 42(5), 1489–1500 (2012)
Wang, X.F., Huang, D.S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recogn. 43(3), 603–618 (2010)
Huang, D.S., Du, J.-X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Netw. 19(12), 2099–2115 (2008)
Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recogn. Artif. Intell. 13(7), 1083–1101 (1999)
Wang, X.-F., Huang, D.S.: A novel density-based clustering framework by using level set method. IEEE Trans. Knowl. Data Eng. 21(11), 1515–1531 (2009)
Huang, D.S., Ip, H.H.S., Chi, Z.-R.: A neural root finder of polynomials based on root moments. Neural Comput. 16(8), 1721–1762 (2004)
Huang, D.S.: A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Trans. Neural Netw. 15(2), 477–491 (2004)
Huang, D.S., Ip, H.H.S., Law, K.C.K., Chi, Z.: Zeroing polynomials using modified constrained neural network approach. IEEE Trans. Neural Netw. 16(3), 721–732 (2005)
Zhu, L., Huang, D.S.: Efficient optimally regularized discriminant analysis. Neurocomputing 117, 12–21 (2013)
Zhu, L., Huang, D.S.: A Rayleigh-Ritz style method for large-scale discriminant analysis. Pattern Recogn. 47, 1698–1708 (2014)
Acknowledgement
This work was supported by the grants of the National Science Foundation of China, Nos. 61520106006, 61532008, 31571364, 61303111, 61411140249, 61402334, 61472280, 61472173, 61572447, 61373098, and 61572364, China Postdoctoral Science Foundation Grant, Nos. 2014M561513 and 2015M580352.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
He, X., Wang, G., Zhang, XP., Shang, L., Huang, ZK. (2016). Leaf Classification Utilizing a Convolutional Neural Network with a Structure of Single Connected Layer. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-42294-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42293-0
Online ISBN: 978-3-319-42294-7
eBook Packages: Computer ScienceComputer Science (R0)