Abstract
Plant identification is an important problem for ecologists, amateur botanists, educators, and so on. Leaf, which can be easily obtained, is usually one of the important factors of plants. In this paper, we propose a growing convolution neural network (GCNN) for plant leaf identification and report the promising results on the ImageCLEF2012 Plant Identification database. The GCNN owns a growing structure which starts training from a simple structure of a single convolution kernel and is gradually added new convolution neurons to. Simultaneously, the growing connection weights are modified until the squared-error achieves the desired result. Moreover, we propose a progressive learning method to determine the number of learning samples, which can further improve the recognition rate. Experiments and analyses show that our proposed GCNN outperforms other state-of-the-art algorithms such as the traditional CNN and the hand-crafted features with SVM classifiers.
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
Arora, A., Gupta, A., Bagmar, N., Mishra, S., Bhattacharya, A.: A plant identification system using shape and morphological features on segmented leaflets: Team iitk, clef 2012. In: CLEF (Online Working Notes/Labs/Workshop) (2012)
Ren, X.-M., Wang, X.-F., Zhao, Y.: An efficient multi-scale overlapped block LBP approach for leaf image recognition. In: Huang, D.-S., Ma, J., Jo, K.-H., Gromiha, M.M. (eds.) ICIC 2012. LNCS, vol. 7390, pp. 237–243. Springer, Heidelberg (2012)
Chen, J., Bai, Y.: Classification of smile expression using hybrid phog and gabor features. In: Computer Application and System Modeling (ICCASM), vol. 12, pp. V12–417. IEEE (2010)
Ma, L.-H., Zhao, Z.-Q., Wang, J.: ApLeafis: an android-based plant leaf identification system. In: Huang, D.-S., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds.) ICIC 2013. LNCS, vol. 7995, pp. 106–111. Springer, Heidelberg (2013)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Sun, B.-Y., Huang, D.-S., Guo, L., Zhao, Z.-Q.: Support vector machine committee for classification. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 648–653. Springer, Heidelberg (2004)
Zhao, Z.Q., Huang, D.S.: A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability. Appl. Math. Modell. 31, 1271–1281 (2007)
Zhao, Z.Q., Huang, D.S., Sun, B.Y.: Human face recognition based on multi-features using neural networks committee. Pattern Recogn. Lett. 25, 1351–1358 (2004)
Zhao, Z.Q., Gao, J., Glotin, H., Wu, X.: A matrix modular neural network based on task decomposition with subspace division by adaptive affinity propagation clustering. Appl. Math. Modell. 34, 3884–3895 (2010)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8, 98–113 (1997)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: 2013 12th International Conference on Document Analysis and Recognition, vol. 2, pp. 958–958. IEEE Computer Society (2003)
Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29, 82–97 (2012)
Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3626–3633. IEEE (2013)
Garcia, C., Delakis, M.: A neural architecture for fast and robust face detection. In: 2002 Proceedings of the 16th International Conference on Pattern Recognition, vol. 2, pp. 44–47. IEEE (2002)
Garcia, C., Delakis, M.: Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1408–1423 (2004)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv preprint arXiv:1207.0580
Ranzato, M., Huang, F.J., Boureau, Y.L., LeCun, Y.: Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2007, pp. 1–8. IEEE (2007)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106, 59–70 (2007)
Theunissen, F.E., Sen, K., Doupe, A.J.: Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J. Neurosci. 20, 2315–2331 (2000)
Zhang, J.R., Zhang, J., Lok, T.M., Lyu, M.R.: A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Appl. Math. Comput. 185, 1026–1037 (2007)
Bouvrie, J.: Notes on convolutional neural networks (2006)
Zheng, P., Zhao, Z.Q., Glotin, H.: Zhaohfut at imageclef 2012 plant identification task. In: CLEF (Online Working Notes/Labs/Workshop), Citeseer (2012)
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Nos. 61375047 and 61272366), the 973 Program of China (No. 2013CB329604), the 863 Program of China (No. 2012AA011005), the Program for Changjiang Scholars and Innovative Research Team in University of the Ministry of Education of China (No. IRT13059), the US National Science Foundation (NSF CCF-0905337), the Faculty Research Grant of Hong Kong Baptist University (No. FRG2/12-13/082), the Hong Kong Scholars Program (No. XJ2012012), China Postdoctoral Science Foundation (No. 2013M540510), and the Fundamental Research Funds for the Central Universities of China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhao, ZQ., Xie, BJ., Cheung, Ym., Wu, X. (2015). Plant Leaf Identification via a Growing Convolution Neural Network with Progressive Sample Learning. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-16808-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16807-4
Online ISBN: 978-3-319-16808-1
eBook Packages: Computer ScienceComputer Science (R0)