Journal of Real-Time Image Processing

, Volume 14, Issue 3, pp 605–616 | Cite as

GLSC: LSC superpixels at over 130 FPS

  • Zhihua Ban
  • Jianguo Liu
  • Jeremy Fouriaux
Special Issue Paper


Superpixel has been successfully applied in various computer vision tasks, and many algorithms have been proposed to generate superpixel map. Recently, a superpixel algorithm called “superpixel segmentation using linear spectral clustering” (LSC) has been proposed, and it performs equally well or better than state-of-the art superpixel segmentation algorithms in terms of several commonly used evaluation metrics in superpixel segmentation. Although LSC is of linear complexity, its original implementation runs in few hundreds of milliseconds for images with resolution of 481 × 321 stated by the authors, which is a limitation for some real-time applications such as visual tracking which may needs, for instance, 30 FPS for standard image resolution (e.g., 480 × 320, 640 × 480, 1280 × 720 and 1920 × 1080). Instead of inventing new algorithms with lower complexity than LSC, we will explore LSC to modify its structure and make it suitable to be implemented by parallel technique. The modified LSC algorithm is implemented in CUDA and tested on several NVIDIA graphics processing unit. Our implementation of the proposed modified LSC algorithm achieves speedups of up to 80× from the original sequential implementation, and the quality, measured by two commonly used evaluation metrics, of our implementation keeps being similar to the original one. The source code will be made publicly available.


Real time GPGPU CUDA Superpixel Image segmentation 


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)CrossRefGoogle Scholar
  3. 3.
    Van den Bergh, M., Boix, X., Roig, G., Van Gool, L.: Seeds: superpixels extracted via energy-driven sampling. Int. J. Comput. Vis. 111(3), 298–314 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  5. 5.
    Garcia-Garcia, A., Orts-Escolano, S., Garcia-Rodriguez, J., Cazorla, M.: Interactive 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors. J. Real Time Image Process. (2016). doi: 10.1007/s11554-016-0607-x Google Scholar
  6. 6.
    Gong, C., Tao, D., Liu, W., Maybank, S.J., Fang, M., Fu, K., Yang, J.: Saliency propagation from simple to difficult. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2531–2539 (2015)Google Scholar
  7. 7.
    Guler, P., Deniz, E.: Real-time multi-camera video analytics system on GPU. J. Real Time Image Process. 11(3), 457–472 (2016). doi: 10.1007/s11554-013-0337-2 CrossRefGoogle Scholar
  8. 8.
    Harris, M.: Optimizing Parallel Reduction in CUDA. NVIDIA Developer Technology (2007)Google Scholar
  9. 9.
    Kesavan, Y., Ramanan, A.: One-pass clustering superpixels. In: 2014 7th International Conference on Information and Automation for Sustainability (ICIAfS), pp. 1–5. IEEE (2014)Google Scholar
  10. 10.
    Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.: Turbopixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)CrossRefGoogle Scholar
  11. 11.
    Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1356–1363 (2015)Google Scholar
  12. 12.
    Liu, X., Xu, Q., Ma, J., Jin, H., Zhang, Y.: MsLRR: a unified multiscale low-rank representation for image segmentation. IEEE Trans. Image Process. 23(5), 2159–2167 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Nguyen, T.V., Lu, C., Sepulveda, J., Yan, S.: Adaptive nonparametric image parsing. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1565–1575 (2015)CrossRefGoogle Scholar
  14. 14.
    NVIDIA Corporation: NVIDIA CUDA Compute Unified Device Architecture Programming Guide. NVIDIA Corporation (2007)Google Scholar
  15. 15.
    Ren, C.Y., Prisacariu, V.A., Reid, I.D.: gSLICr: SLIC superpixels at over 250Hz. ArXiv e-prints (2015)Google Scholar
  16. 16.
    Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proceedings. Ninth IEEE International Conference on Computer Vision, 2003, pp. 10–17 (2003)Google Scholar
  17. 17.
    Sun, X., Shang, K., Ming, D., Tian, J., Ma, J.: A biologically-inspired framework for contour detection using superpixel-based candidates and hierarchical visual cues. Sensors 15(10), 26654–26674 (2015)CrossRefGoogle Scholar
  18. 18.
    Wang, S., Lu, H., Yang, F., Yang, M.H.: Superpixel tracking. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1323–1330 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.National Key laboratory of Science and Technology on Multi-spectral Information Processing, School of AutomationHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations