Image Classification Using Deep Learning and Fuzzy Systems

  • Chandrasekar RaviEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


Classification of images is a significant step in pattern recognition and digital image processing. It is applied in various domains for authentication, identification, defense, medical diagnosis and so on. Feature extraction is an important step in image processing which decides the quality of the model to be built for image classification. With the abundant increase in data now-a-days, the traditional feature extraction algorithms are finding difficulty in coping up with extracting quality features in finite time. Also the learning models developed from the extracted features are not so easily interpretable by the humans. So, considering the above mentioned arguments, a novel image classification framework has been proposed. The framework employs a pre-trained convolution neural network for feature extraction. Brain Storm Optimization algorithm is designed to learn the classification rules from the extracted features. Fuzzy rules based classifier is used for classification. The proposed framework is applied on Caltech 101 dataset and evaluated using accuracy of the classifier as the performance metric. The results demonstrate that the proposed framework outperforms the traditional feature extraction based classification techniques by achieving better accuracy of classification.


Image classification Deep learning Fuzzy systems 


  1. 1.
    Ballard, D.H., Brown, C.M.: Computer Vision. Prentice Hall, Upper Saddle River (1982)Google Scholar
  2. 2.
    Huang, T., Vandoni, C.: Computer Vision: Evolution and Promise. 19th CERN School of Computing, pp. 21–25. CERN, Geneva (1996)Google Scholar
  3. 3.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Thomson, Pacific Grove (2008)Google Scholar
  4. 4.
  5. 5.
    Murphy, M.: Star Trek’s tricorder medical scanner just got closer to becoming a realityGoogle Scholar
  6. 6.
    Yang, X., Gao, X., Song, B., Yang, D.: Aurora image search with contextual CNN feature. Neurocomputing 281, 67–77 (2018)CrossRefGoogle Scholar
  7. 7.
    Zhang, M., Li, W., Du, Q., Gao, L., Zhang, B.: Feature extraction for classification of hyperspectral and LiDAR data using patch-to-patch CNN. IEEE Trans. Cybern. (2018). Early AccessGoogle Scholar
  8. 8.
    Kong, Y., Wang, X., Cheng, Y.: Spectral–spatial feature extraction for HSI classification based on supervised hypergraph and sample expanded CNN. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 4128–4140 (2018). Early AccessCrossRefGoogle Scholar
  9. 9.
    Liu, B., Yu, X., Zhang, P., Yu, A., Fu, Q., Wei, X.: Supervised deep feature extraction for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(4), 1909–1921 (2018)CrossRefGoogle Scholar
  10. 10.
    Ding, Y., Deng, R., Xie, X., Xu, X., Zhao, Y., Chen, X., Krylov, A.S.: No-reference stereoscopic image quality assessment using convolutional neural network for adaptive feature extraction. IEEE Access 6, 37595–37603 (2018)CrossRefGoogle Scholar
  11. 11.
    Wang, X., Chen, C., Cheng, Y., Wang, Z.J.: Zero-shot image classification based on deep feature extraction. IEEE Trans Cogn. Dev. Syst 10(2), 432–444 (2018)CrossRefGoogle Scholar
  12. 12.
    Lv, Y., Zhou, W., Tian, Q., Sun, S., Li, H.: Retrieval oriented deep feature learning with complementary supervision mining. IEEE Trans. Image Process. 27(10), 4945–4957 (2018)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wen, T., Zhang, Z.: Deep convolution neural network and autoencoders-based unsupervised feature learning of EEG signals. IEEE Access 6, 25399–25410 (2018)CrossRefGoogle Scholar
  14. 14.
    Ye, F., Su, Y., Xiao, H., Zhao, X., Min, W.: Remote sensing image registration using convolutional neural network features. IEEE Geosci. Remote Sens. Lett. 15(2), 232–236 (2018)CrossRefGoogle Scholar
  15. 15.
    Yang, Z., Dan, T., Yang, Y.: Multi-temporal remote sensing image registration using deep convolutional features. IEEE Access 6, 38544–38555 (2018)CrossRefGoogle Scholar
  16. 16.
    Nguyen, K., Fookes, C., Ross, A., Sridharan, S.: Iris recognition with off-the-shelf CNN features: a deep learning perspective. IEEE Access 6, 18848–18855 (2018)CrossRefGoogle Scholar
  17. 17.
    Claessens, B.J., Vrancx, P., Ruelens, F.: Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control. IEEE Tran. Smart Grid 9(4), 3259–3269 (2018)CrossRefGoogle Scholar
  18. 18.
    Hao, W., Bie, R., Guo, J., Meng, X., Wang, S.: Optimized CNN based image recognition through target region selection. Optik-Int. J. Light Electron Opt. 156, 772–777 (2018)CrossRefGoogle Scholar
  19. 19.
    Yu, W., Sun, X., Yang, K., Rui, Y., Yao, H.: Hierarchical semantic image matching using CNN feature pyramid. Comput. Vis. Image Underst. 169, 40–51 (2018)CrossRefGoogle Scholar
  20. 20.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: IEEE CVPR 2004, Workshop on Generative-Model Based Vision (2004)Google Scholar
  21. 21.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: LSVRC 2015 (2015)Google Scholar
  22. 22.
    Ojala, T., Pietikäinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582–585 (1994)Google Scholar
  23. 23.
    Shi, Y.: Brain storm optimization algorithm. In: Advances in Swarm Intelligence, LNCS, vol. 6728, pp. 303–309 (2011)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Institute of Technology PuducherryKaraikalIndia

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