A Dynamic Region Generation Algorithm for Image Segmentation Based on Spiking Neural Network

  • Lin Zuo
  • Linyao Ma
  • Yanqing Xiao
  • Malu Zhang
  • Hong Qu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)


We propose a dynamic region generation algorithm for image segmentation based on spiking neural network inspired by human visual cortex that shows the tremendous capacity of processing image. The network structure generated by the proposed algorithm is automatically and dynamically. An image can be decomposed into several different shape and size of regions that look like superpixels. Merging these regions based on the color space similarity can extract contour. Dynamic network architecture brings stronger computing power. Dynamic generation method leads to more flexible network. Experimental results on BCDS300 dataset confirm that our approach achieves satisfactory segmentation results for different images compared with SLIC.


Spiking neural network Image segmentation Pattern recognition 



This work was supported in part by the National Science Foundation of China under Grants 61573081, 61273308 and the Fundamental Research Funds for Central Universities under Grant ZYGX2015J062.


  1. 1.
    Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural. Syst. 19, 295–308 (2009)CrossRefGoogle Scholar
  2. 2.
    Qu, H., Xie, X., Liu, Y., Zhang, M., Lu, L.: Improved perception-based spiking neuron learning rule for real-time user authentication. Neurocomputing 151, 310–318 (2015)CrossRefGoogle Scholar
  3. 3.
    De Berredo, R.C.: A review of spiking neuron models and applications. Doctoral dissertation, M.Sc. Dissertation, University of Minas Gerais (2005)Google Scholar
  4. 4.
    Wolters, A., Sandbrink, F., Schlottmann, A., Kunesch, E., Stefan, K., Cohen, L.G., Classen, J.: A temporally asymmetric Hebbian rule governing plasticity in the human motor cortex. J. Neurophysiol. 89, 2339–2345 (2003)CrossRefGoogle Scholar
  5. 5.
    Masquelier, T., Guyonneau, R., Thorpe, S.J.: Competitive STDP-based spike pattern learning. Neural. Comput. 21, 1259–1276 (2009)CrossRefMATHGoogle Scholar
  6. 6.
    Qu, H., Yang, S.X., Willms, A.R., Yi, Z.: Real-time robot path planning based on a modified pulse-coupled neural network model. IEEE Trans. Neural. Netw. 20, 1724–1739 (2009)CrossRefGoogle Scholar
  7. 7.
    Xie, X., Qu, H., Yi, Z., Kurths, J.: Efficient training of supervised spiking neural network via accurate synaptic-efficiency adjustment method. IEEE Trans. Neural Netw. Learn. Syst. 28, 1411–1424 (2017)CrossRefGoogle Scholar
  8. 8.
    Zhang, M., Qu, H., Belatreche, A., Xie, X.: EMPD: an efficient membrane potential driven supervised learning algorithm for spiking neurons. IEEE Trans. Cogn. Dev. Syst (2017)Google Scholar
  9. 9.
    Ghosh-Dastidar, S., Adeli, H.: Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integr. Comput. Aided. Eng. 14, 187–212 (2007)Google Scholar
  10. 10.
    Meng, Y., Jin, Y., Yin, J.: Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition. IEEE Trans. Neural. Netw. 22, 1952–66 (2011)CrossRefGoogle Scholar
  11. 11.
    Ang, C.H., Jin, C., Leong, P.H., van Schaik, A.: Spiking neural network-based auto-associative memory using FPGA interconnect delays. In: Field-Programmable Technology, pp. 1–4. IEEE Press (2011)Google Scholar
  12. 12.
    Wu, Q., McGinnity, T.M., Maguire, L., Cai, R., Chen, M.: A visual attention model based on hierarchical spiking neural networks. Neurocomputing 116, 3–12 (2013)CrossRefGoogle Scholar
  13. 13.
    Lin, X., Wang, X., Cui, W.: An automatic image segmentation algorithm based on spiking neural network model. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2014. LNCS, vol. 8588, pp. 248–258. Springer, Cham (2014). doi: 10.1007/978-3-319-09333-8_27 Google Scholar
  14. 14.
    Sun, Q.Y., Wu, Q.X., Wang, X., Hou, L.: Fruit Image Segmentation Based on a Colour Perception Neural Network Inspired by the Retina Structure. ATLANTIS Press (2015)Google Scholar
  15. 15.
    Kerr, D., McGinnity, T.M., Coleman, S., Clogenson, M.: A biologically inspired spiking model of visual processing for image feature detection. Neurocomputing 158, 268–280 (2015)CrossRefGoogle Scholar
  16. 16.
    Afifi, A., Ayatollahi, A., Raissi, F.: Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits. In: IEEE ECCTD 2009, pp. 563–566. IEEE Press (2009)Google Scholar
  17. 17.
    Gerstner, W., Kistler, M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)CrossRefMATHGoogle Scholar
  18. 18.
    Hosoya, T., Baccus, S.A., Meister, M.: Dynamic predictive coding by the retina. Nature 436, 71 (2005)CrossRefGoogle Scholar
  19. 19.
    Van de Sande, K.E., Uijlings, J.R., Gevers, T., Smeulders, A.W.: Segmentation as selective search for object recognition. IEEE ICCV 2011, pp. 1879–1886 (2011)Google Scholar
  20. 20.
  21. 21.
    Alpert, S., Galun, M., Brandt, A., Basri, R.: Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans. Pattern Anal. Mach. Intell. 34, 315–327 (2012)CrossRefGoogle Scholar
  22. 22.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Ssstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lin Zuo
    • 1
  • Linyao Ma
    • 1
  • Yanqing Xiao
    • 1
  • Malu Zhang
    • 1
  • Hong Qu
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

Personalised recommendations