Abstract
Recent theoretical studies indicate that Deep Neural Network has been applied to many image processing tasks. However, learning in deep architectures is still difficult. One of the neural network, Convolutional Neural Network (CNN) has gained great success in image recognition and it builds features by automatic learning. More importantly, CNN can operate directly on the gray image, so it can be directly used for processing classification of the image. In order to utilize CNN to recognize plant leaf, a hierarchical model based on CNN is proposed in this paper. We firstly do some pre-processing, such as illumination changes, rotation and leaf distortion. After that, we applied the method of CNN to extract the features of leaves pictures. One focus on our network is about the depth of CNN, which affects the ability of capability of convolution. Thus, we try our best to choose the best depth of CNN with several experiments. Moreover, in order to destroy the symmetry of networks, the strategies used in this paper is to add a mathematical formula for feature map connection between convolutional layer and sampling layer. The experimental results show that the proposed method is quite effective and feasible. And we also applied other classification methods to the ICL dataset. By contrast, our classification is much better than other methods.
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References
Cai, C.Z., et al.: SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 31(13), 3692–3697 (2003)
Christian, S., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004, ICPR 2004. vol. 3, IEEE (2004)
Mavroforakis, M.E., Theodoridis, S.: A geometric approach to support vector machine (SVM) classification. IEEE Trans. Neural Netw. 17(3), 671–682 (2006)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives, 1–1 (2013)
Lecun, Y., Bottou, L., Bengio, Y.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practice for convolutional neural networks applied to visual document analysis. In: ICDAR, pp. 958–962. IEEE, Los Alamitos (2003)
Cires, D.C., Meier, U., Masci, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2, pp. 1237–1242 (2011)
Cires, D.C., Meier, U., Schmidhuber, et al.: Multi-column deep neural networks for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649, New York (2012)
Baum, E., Haussler, D.: What size net gives valid generalization (J). Neural Comput. 1, 151–160 (1989)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet Classification with Deep Convolutional Neural Networks. In: NIPS (2012)
Duda, R., Hard, P., Stork, D.: Pattern Recognition, 2nd edn. Wiley-Interscience, New York (2000)
Behnke, S.: Hierarchical Neural Networks for Image Interpretation [M], 1(4), 541–551 (1989)
Lippmann, R.: An introduction to computing with neural nets [J]. IEEE ASSP Magazine 4, 22 (1987)
Bourlard, H., Kamp, Y.: Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59(4–5), 291–294 (1988)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising AEs. In: ICML (2008)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stackeddenoising AEs: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Li, B., Huang, D.S.: Locally linear discriminant embedding: an efficient method for face recognition. Pattern Recogn. 41(12), 3813–3821 (2008)
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., Horace, H.S.Ip, Chi, Z.-R.: A neural root finder of polynomials based on root moments. Neural Comput. 16(8), 1721–1762 (2004)
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)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition (in Chinese). Publishing House of Electronic Industry of China, Beijing (1996)
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)
Acknowledgments
The authors would like to sincerely thank the Institute of Machine Learning and Systems Biology of Tongji University and Professor Guo-Wei Yang (No.61272077).
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Yang, GW., Jing, HF. (2015). Multiple Convolutional Neural Network for Feature Extraction. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_10
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DOI: https://doi.org/10.1007/978-3-319-22186-1_10
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