Abstract
To solve the main problem of recognition accuracy, many image classification models have been implemented. A lot of attention was paid to Machine Learning. In this work, we will examine the problem of image classification related on transmission training to study whether it will work better in point of accuracy and efficiency with new sets of image data through Transfer Learning. Transfer Learning is a method of using the knowledge of a pre-trained model in another task. In this article, we will compare the image classification results of Logistic Regression (LR), Linear SVM and Random Forest Classifiers (RFC) using the pre – trained VGG-16 model. Image classification problem is implemented using Caltech - 101 and Flowers - 17 datasets.
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
Jason, Y., Jeff, C., Yoshua B., Hod L.: How transferable are features in deep neural networks? (2014). ar**v:1411.1792[cs.LG]
Marimuthu, S., Roomi, S.M.: Particle swarm optimized fuzzy model for the classification of banana ripeness. IEEE Sens. J. 17(15), 4903–4915 (2017)
Devi, P.L., Varadarajan, S.: Defect fruit image analysis using advanced bacterial foraging optimizing algorithm. IOSR J. Comput. Eng. 14(1), 22–26 (2013)
Huang, T., Yang, R., Huang, W., Huang, Y., Qiao, X.: Detecting sugarcane borer diseases using support vector machine. Inform. Process. Agric. 5(1), 74–82 (2018)
Sharma, P., Berwal, YPS., Ghai W.: Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Inform. Process. Agric. 1–9 (2019). https://doi.org/10.1016/j.inpa.2019.11.001. (in press)
Ji, M., Zhang, L., Wu, Q.: Automatic grape leaf diseases identification via united model based on multiple convolutional neural networks. Inform. Process. Agric. (2019). https://doi.org/10.1016/j.inpa.2019.10.003
Guo, X., Zhao, X., Liu, Y., Li, D.: Underwater sea cucumber identification via deep residual networks. Inform. Process. Agric. 6(3), 307–315 (2019)
Muhammad, H.A., Abdul, B.: Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Inform. Process. Agric. (2019). https://doi.org/10.1016/j.inpa.2019.12.002
Gardashova, L.A., Gahramanli, Y., Babanli, M.: Fuzzy neural network based analysis of the process of oil product sorption with foam polystyrene. Int. J. Eng. Res. Appl. 7(9), 85–90 (2017)
Behera, S.K., Rath, A.K., Sethy, P.K.: Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Inform. Process. Agric. (2020). https://doi.org/10.1016/j.inpa.2020.05.003
Li, W., Gu, S., Zhang, X., Chen, T.: Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes. https://doi.org/10.1016/j.compchemeng.2020.106904
Kumari, P., Seeja, K.R.: Periocular biometrics for non-ideal images: with off-the-shelf Deep CNN & Transfer Learning Approach. https://doi.org/10.1016/j.procs.2020.03.234
Hussain, M., Bird, J., Faria, D.: A Study on CNN Transfer Learning for Image Classification. Advances in Computational Intelligence System (2020). (in press)
Kafeng, W., **tong, G., Yiren, Z., Li, X., De**g, D., Cheng-Zhong, X.: Pay attention to features, transfer learn faster CNNs. In: International Conference on Learning Representations. https://openreview.net/forum?id=ryxyCeHtPB
Rodríguez, G.: Lecture Notes on Generalized Linear Models. http://data.princeton.edu/wws509/notes/
Cortes, C., Vapnik, V.N.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018. S2CID: 206787478
Li, F.-F., Fergus, R., Pietro, 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)
Nilsback, M.-E., Andrew, Z.A.: Visual vocabulary for flower classification. In: CVPR (2), pp. 1447–1454. IEEE Computer Society (2006). http://dblp.uni-trier.de/db/conf/cvpr/cvpr2006-2.html#NilsbackZ06
Goodfellow, I., Yoshua B., Aaron,C.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zeynallı, E. (2021). Analysis the Image Classification Problem Based on Transfer Learning. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds) 14th International Conference on Theory and Application of Fuzzy Systems and Soft Computing – ICAFS-2020 . ICAFS 2020. Advances in Intelligent Systems and Computing, vol 1306. Springer, Cham. https://doi.org/10.1007/978-3-030-64058-3_87
Download citation
DOI: https://doi.org/10.1007/978-3-030-64058-3_87
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64057-6
Online ISBN: 978-3-030-64058-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)