Skip to main content
SpringerLink
Log in

GMMSP on GPU

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Superpixel segmentation is a fundamental task in computer vision. Existing works contribute to superpixel segmentation either by improving segmentation accuracy or by reducing execution time. The former modifies existing models or develops new models to improve accuracy. The latter accelerates existing implementations or reduces algorithm complexity to improve execution rate. This work falls into the second category. Recently, a superpixel algorithm using Gaussian mixture model (GMMSP) achieves state-of-the-art performance in accuracy. After exploring this algorithm, we reached new conclusions on GMMSP that unlock potential concerning fine-grain parallelism implementation. We implement GMMSP with CUDA and make it run on GPUs. Experiments are conducted to validate the consistency between CPU and GPU implementations and to evaluate the performance of our implementation with respect to a serial and an OpenMP implementation. When we consider a full implementation with a postprocessing step executed on CPU to guarantee connectivity constraint, the proposed implementation achieves a speedup of 21× compared to the OpenMP implementation for images of size 240 × 320, using NVIDIA GTX 1080. It is also mentionable that we achieve a performance of over 1000 FPS on GTX 1080 (speedup of 77× compared to the OpenMP implementation) if the connectivity constraint is not included.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://github.com/ahban/OpenGLSC.

  2. https://github.com/ahban/GMMSP.

  3. Refer to [4] about the implementation of color conversion.

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Ban, Z., Liu, J., Cao, L.: Superpixel segmentation using Gaussian mixture model (2016). arXiv preprint arXiv:1612.08792

  4. Ban, Z., Liu, J., Fouriaux, J.: GLSC: LSC superpixels at over 130 fps. J. Real-Time Image Process. 1–12 (2016)

  5. Derue, F., Bilodeau, G., Bergevin, R.: Spikes: Superpixel-keypoints structure for robust visual tracking (2016). CoRR abs/1610.07238

  6. 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). https://doi.org/10.1007/s11554-016-0607-x

    Article  Google Scholar 

  7. Guler, P., Deniz, E.: Real-time multi-camera video analytics system on GPU. J. Real-Time Image Process. 11(3), 457–472 (2016). https://doi.org/10.1007/s11554-013-0337-2

    Article  Google Scholar 

  8. Kesavan, Y., Ramanan, A.: One-pass clustering superpixels. In: Proceedings of the International Conference on Information and Automation for Sustainability, pp. 1–5 (2014)

  9. 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)

    Article  Google Scholar 

  10. Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1356–1363 (2015)

  11. Li, Z., Wu, X.M., Chang, S.F.: Segmentation using superpixels: a bipartite graph partitioning approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 789–796 (2012)

  12. Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097–2104 (2011)

  13. Lu, Z., Fu, Z., Xiang, T., Han, P., Wang, L., Gao, X.: Learning from weak and noisy labels for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(3), 486–500 (2017)

    Article  Google Scholar 

  14. Ma, J., Zhao, J., Yuille, A.L.: Non-rigid point set registration by preserving global and local structures. IEEE Trans. Image Process. 25(1), 53–64 (2016)

    Article  MathSciNet  Google Scholar 

  15. Machairas, V., Faessel, M., Cardenas-Pena, D., Chabardes, T., Walter, T., Decenciere, E.: Waterpixels. IEEE Trans. Image Process. 24(11), 3707–3716 (2015)

    Article  MathSciNet  Google Scholar 

  16. Mei, X., Chu, X.: Dissecting GPU memory hierarchy through microbenchmarking. IEEE Trans. Parallel Distrib. Syst. 28(1), 72–86 (2017)

    Article  Google Scholar 

  17. Mori, G.: Guiding model search using segmentation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1417–1423 (2005)

  18. Nguyen, T.V., Lu, C., Sepulveda, J., Yan, S.: Adaptive nonparametric image parsing. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1565–1575 (2015)

    Article  Google Scholar 

  19. NVIDIA: CUDA toolkit documentation. https://docs.nvidia.com/cuda/ (2017)

  20. Ren, C.Y., Prisacariu, V.A., Reid, I.D.: gSLICr: SLIC superpixels at over 250Hz (2015). ArXiv e-prints

  21. Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014)

    Article  MathSciNet  Google Scholar 

  22. Shu, G., Dehghan, A., Shah, M.: Improving an object detector and extracting regions using superpixels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3721–3727 (2013)

  23. 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)

    Article  MathSciNet  Google Scholar 

  24. Wang, J., Wang, X.: VCells: simple and efficient superpixels using edge-weighted centroidal voronoi tessellations. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1241–1247 (2012)

    Article  Google Scholar 

  25. Yan, J., Yu, Y., Zhu, X., Lei, Z., Li, S.Z.: Object detection by labeling superpixels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5107–5116 (2015)

  26. Yang, F., Lu, H., Yang, M.H.: Robust superpixel tracking. IEEE Trans. Image Process. 23(4), 1639–1651 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Key laboratory of Science and Technology on Multi-spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei Province, China

    Zhihua Ban, Jianguo Liu & Jeremy Fouriaux

Authors
  1. Zhihua Ban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianguo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeremy Fouriaux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhihua Ban.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ban, Z., Liu, J. & Fouriaux, J. GMMSP on GPU. J Real-Time Image Proc 17, 245–257 (2020). https://doi.org/10.1007/s11554-018-0762-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-018-0762-3

Keywords

Search

Navigation