Advertisement

A Visual Recognition Model Based on Hierarchical Feature Extraction and Multi-layer SNN

  • Xiaoliang Xu
  • Wensi Lu
  • Qiming Fang
  • Yixing Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

In this paper, a visual pattern recognition model is proposed, which effectively combines hierarchical feature extraction model and coding method on multi-layer SNN. This paper takes HMAX model as feature extraction model and adopts independent component analysis (ICA) to improve it, so that the model can satisfy the sparsity of information extraction and the output result is more suitable for SNN processing. Multi-layer SNN is used as classifier and the firing of spikes is not limited in the learning process. We use valid phase coding to connect these two parts. Through the experiments on the MNIST and Caltech101 datasets, it can be found that the model has good classification performance.

Keywords

Visual pattern recognition Multi-layer SNN HMAX Phase coding 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61603119 and Zhejiang Provincial Natural Science Foundation of China under Grant No. LY17F020028.

References

  1. 1.
    Mély, D.A., Serre, T.: Towards a theory of computation in the visual cortex. In: Zhao, Q. (ed.) Computational and Cognitive Neuroscience of Vision. CST, pp. 59–84. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-0213-7_4CrossRefGoogle Scholar
  2. 2.
    Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019 (1999)CrossRefGoogle Scholar
  3. 3.
    Liu, C., Sun, F.: HMAX model: a survey. In: Neural Networks (IJCNN), pp. 1–7. IEEE (2015)Google Scholar
  4. 4.
    Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: 2005 IEEE Computer Society Conference Computer Vision and Pattern Recognition. CVPR 2005, vol. 2, pp. 994–1000 (2005)Google Scholar
  5. 5.
    Serre, T., Wolf, L., Bileschi, S., et al.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29, 411–426 (2007)CrossRefGoogle Scholar
  6. 6.
    Mutch, J., Lowe, D.G.: Object class recognition and localization using sparse features with limited receptive fields. Int. J. Comput. Vis. 80, 45–57 (2008)CrossRefGoogle Scholar
  7. 7.
    Hu, X., Zhang, J., Li, J., et al.: Sparsity-regularized HMAX for visual recognition. Plos One 9, e81813 (2014)CrossRefGoogle Scholar
  8. 8.
    Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32, 379–390 (2014)CrossRefGoogle Scholar
  9. 9.
    Dura-Bernal, S., Wennekers, T., Denham, S.L.: Modelling object perception in cortex: hierarchical Bayesian networks and belief propagation. In: 2011 45th Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE (2011)Google Scholar
  10. 10.
    Sufikarimi, H., Mohammadi, K.: Speed up biological inspired object recognition, HMAX. In: Intelligent Systems and Signal Processing (ICSPIS) (2017)Google Scholar
  11. 11.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)CrossRefGoogle Scholar
  12. 12.
    Zheng, Y., et al.: Sparse temporal encoding of visual features for robust object recognition by spiking neurons. IEEE Trans. Neural Netw. Learn. Syst. 1–11 (2018).  https://doi.org/10.1109/TNNLS.2018.2812811
  13. 13.
    Yu, Q., Tang, H., Tan, K.C., et al.: Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns. Plos One 8, e78318 (2013)CrossRefGoogle Scholar
  14. 14.
    Bohte, S.M., Kok, J.N., La Poutre, H.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48, 17–37 (2002)CrossRefGoogle Scholar
  15. 15.
    Caporale, N., Dan, Y.: Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008)CrossRefGoogle Scholar
  16. 16.
    Sporea, I., Grüning, A.: Supervised learning in multilayer spiking neural networks. Neural Comput. 25, 473–509 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Pyle, R., Rosenbaum, R.: Spatiotemporal dynamics and reliable computations in recurrent spiking neural networks. Phys. Rev. Lett. 118(1), 018103 (2017)CrossRefGoogle Scholar
  18. 18.
    Gardner, B., Sporea, I., Grüning, A.: Learning spatiotemporally encoded pattern transformations in structured spiking neural networks. Neural Comput. 27, 2548–2586 (2015)CrossRefGoogle Scholar
  19. 19.
    Nadasdy, Z.: Information encoding and reconstruction from the phase of action potentials. Front. Syst. Neurosci. 3, 6 (2009)CrossRefGoogle Scholar
  20. 20.
    Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res. 37, 3311–3325 (1997)CrossRefGoogle Scholar
  21. 21.
    Rossum, M.V.: A novel spike distance. Neural Comput. 13, 751–763 (2001)CrossRefGoogle Scholar
  22. 22.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Computer Science (2014)Google Scholar
  23. 23.
    Xu, X., Jin, X., Yan, R., et al.: Visual pattern recognition using enhanced visual features and PSD-based learning rule. IEEE Trans. Cogn. Dev. Syst. 10, 205–212 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaoliang Xu
    • 1
  • Wensi Lu
    • 1
  • Qiming Fang
    • 1
  • Yixing Xia
    • 1
  1. 1.School of ComputerHangzhou Dianzi UniversityHangzhouChina

Personalised recommendations