Abstract
Automatic recognition of tropical wood species is a very challenging task due to the lack of discriminative features among intra wood species and very discriminative features among inter class species. While many conventional pattern recognition algorithms have been implemented and proven to solve wood image classification with 100% accuracy, when using deep learning however, the classification accuracy drops tremendously to only 36.3% due to small number of training samples. Deep learning requires large number of samples in order to work well, unfortunately, wood samples provided by the national forest institute are limited. In this paper, we explore the use of transfer learning in deep neural network for the classification of tropical wood species based on image analysis. Several model of deep learning techniques are tested and results have shown that the classification performance after transfer learning was added reaches 100% accuracy.
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
Similar content being viewed by others
References
Khalid, M., Lew, E., Lee, Y.I., Yusof, R.: Design of an intelligent wood species recognition system. Most 9(3), 9–17 (2008)
Nasirzadeh, M., Khazael, A.A., Bin Khalid, M.: Woods recognition system based on local binary pattern. In: 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks, (2), pp. 308–313 (2010)
Yusof, R., Khairuddin, U.: A new mutation operation for faster convergence in genetic algorithm feature selection. Int. J. Innov. Comput., Inf. Control. (IJICIC), 8(10(B)), 7363–7379 (2012)
Yusof, R., Khairuddin, U., Rosli, N.R., Abdul Ghafar, H., Nik Azmi, N.A., Ahmad, A. Mohd Khairuddin, A.S.: A study of feature extraction and classifier methods for tropical wood recognition system. IEEE TENCON2018, pp 1–6 (2018)
Wang, Z., Li, C., Shao, H., Sun, J.: Eye recognition with mixed convolutional and residual network (MiCoRe-Net). IEEE Access 6, 17905–17912 (2018)
Gomez-Rios, A., Tabik, S., Luengo, J., Shihavuddin, A.S.M., Krawczyk, B., Herrera, F.: Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. Expert Syst. Appl. 118, 315–328 (2019)
Soudani, A., Barhoumi, W.: An image-based segmentation recommender using crowd sourcing and transfer learning for skin lesion extraction. Expert Syst. Appl. 118, 400–410 (2019)
Tian, J., Li, Y.X.: Convolutional neural networks for steganalysis via transfer learning. Int. J. Pattern Recognit. Artif. Intell. 33(2) (2019)
Xian, Y., Hu, H.: Enhanced multi-dataset transfer learning method for unsupervised person re-identification using co-training strategy. IET Comput. Vision 12(8), 1219–1227 (2018)
Byra, M., Styczynski, G., Szmigielski, C., Kalinowski, P., Michalowski, L., Paluszkiewicz, R., Bogna, Z.W., Krzysztof, Z., Piotr, S., Andrzej, N.: Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Comput. Assist. Radiol. Surg. 13, 1895–1903 (2018)
Qin, C.X., Qu, D., Zhang, L.H.: Towards end-to-end speech recognition with transfer learning. EURASIP J. Audio Speech Music. Process. (2018). https://doi.org/10.1186/s13636-018-0141-9
Wang, J.T., Yan, G.L., Wang, H.Y., Hua, J.: Pedestrian recognition in multi-camera networks based on deep transfer learning and feature visualization. Neurocomputing 316, 166–177 (2018)
Kim, S.J., Wang, C., Zhao, B., Im, H., Min, J., Choi, H.J., Tadros, J., Choi, N.R., Castro, C.M., Weissleder, R., Lee, H., Lee, K.: Deep transfer learning-based hologram classification for molecular diagnostics. Sci. Rep. 8(17003) (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. arXiv:1603.05027 (2016)
Abdi, M., Nahavandi, S.: Multi-residual networks. arXiv:1609.05672 [cs], September (2016)
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778) (2016)
LeCun, Y., Cortes, C., Burges, C.: Mnist handwritten digit database. AT&T Labs. http://yann.lecun.com/exdb/mnist (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yusof, R., Ahmad, A., Khairuddin, A.S.M., Khairuddin, U., Azmi, N.M.A.N., Rosli, N.R. (2020). Transfer Learning Approach in Automatic Tropical Wood Recognition System. In: Okada, H., Atluri, S. (eds) Computational and Experimental Simulations in Engineering. ICCES 2019. Mechanisms and Machine Science, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-030-27053-7_104
Download citation
DOI: https://doi.org/10.1007/978-3-030-27053-7_104
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27052-0
Online ISBN: 978-3-030-27053-7
eBook Packages: EngineeringEngineering (R0)