Advertisement

Computational Visual Media

, Volume 4, Issue 4, pp 333–348 | Cite as

FLIC: Fast linear iterative clustering with active search

  • Jiaxing Zhao
  • Ren Bo
  • Qibin Hou
  • Ming-Ming Cheng
  • Paul Rosin
Open Access
Research Article
  • 61 Downloads

Abstract

In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm called “active search” which explicitly considers neighbor continuity. Based on this search method, we design a back-and-forth traversal strategy and a joint assignment and update step to speed up the algorithm. Compared to earlier methods, such as simple linear iterative clustering (SLIC) and its variants, which use fixed search regions and perform the assignment and the update steps separately, our novel scheme reduces the number of iterations required for convergence, and also provides better boundaries in the over-segmentation results. Extensive evaluation using the Berkeley segmentation benchmark verifies that our method outperforms competing methods under various evaluation metrics. In particular, our method is fastest, achieving approximately 30 fps for a 481 × 321 image on a single CPU core. To facilitate further research, our code is made publicly available.

Keywords

image over-segmentation SLIC neighbor continuity back-and-forth traversal 

Notes

Acknowledgements

This research was sponsored by National Natural Science Foundation of China (Nos. 61620106008 and 61572264), Huawei Innovation Research Program (HIRP), and IBM Global SUR Award.

References

  1. [1]
    Cheng, M.-M.; Liu, Y.; Hou, Q.; Bian, J.; Torr, P.; Hu, S.-M.; Tu, Z. HFS: Hierarchical feature selection for efficientimage segmentation. In: Computer Vision–ECCV 2016. Lecture Notes in Computer Science, Vol. 9907. Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Eds. Springer Cham, 867–882, 2016.Google Scholar
  2. [2]
    Wang, Z.; Feng, J.; Yan, S.; Xi, H. Image classificationvia object-aware holistic superpixel selection. IEEE Transactions on Image Processing Vol. 22, No. 11, 4341–4352, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  3. [3]
    Hoiem, D.; Efros, A. A.; Hebert, M. Automatic photopop-up. ACM Transactions on Graphics Vol. 24, No. 3, 577–584, 2005.CrossRefGoogle Scholar
  4. [4]
    Wang, S.; Lu, H.; Yang, F.; Yang, M.-H. Superpixeltracking. In: Proceedings of the IEEE International Conference on ComputerVision, 1323–1330, 2011.Google Scholar
  5. [5]
    Felzenszwalb, P. F.; Huttenlocher, D. P. Efficient-graph-based image segmentation. International Journal of Computer Vision Vol. 59, No. 2, 167–181, 2004.CrossRefGoogle Scholar
  6. [6]
    Comaniciu, D.; Meer, P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24, No. 5, 603–619, 2002.CrossRefGoogle Scholar
  7. [7]
    Vincent, L.; Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 13, No. 6, 583–598, 1991.CrossRefGoogle Scholar
  8. [8]
    Shi, J.; Malik, J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 22, No. 8, 888–905, 2000.CrossRefGoogle Scholar
  9. [9]
    Boykov, Y.; Veksler, O.; Zabih, R. Fast approxi-mate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 23, No. 11, 1222–1239, 2001.CrossRefGoogle Scholar
  10. [10]
    Veksler, O.; Boykov, Y.; Mehrani, P. Superpixels and supervoxels in an energy optimization framework. In: Computer Vision–ECCV 2010. Lecture Notes in Computer Science, Vol. 6315. Daniilidis, K.; Maragos, P.; Paragios, N. Eds. Springer Berlin Heidelberg, 211–224, 2010.Google Scholar
  11. [11]
    Boykov, Y.; Kolmogorov, V. An experimentalcomparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 26, No. 9, 1124–1137, 2004.CrossRefGoogle Scholar
  12. [12]
    Kolmogorov, V.; Zabin, R. What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 26, No. 2, 147–159, 2004.CrossRefGoogle Scholar
  13. [13]
    Levinshtein, A.; Stere, A.; Kutulakos, K. N.; Fleet, D. J.; Dickinson, S. J.; Siddiqi, K. TurboPixels: Fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 31, No. 12, 2290–2297, 2009.CrossRefGoogle Scholar
  14. [14]
    Osher, S.; Sethian, J. A. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics Vol. 79, No. 1, 12–49, 1988.MathSciNetCrossRefzbMATHGoogle Scholar
  15. [15]
    Van den Bergh, M.; Boix, X.; Roig, G.; de Capitani, B.; Van Gool, L. SEEDS: Superpixels extracted via energy-drivensampling. In: Computer Vision–ECCV 2012. Lecture Notes in Computer Science, Vol. 7578. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 13–26, 2012.Google Scholar
  16. [16]
    Liu, M.-Y.; Tuzel, O.; Ramalingam, S.; Chellappa, R. Entropy rate superpixel segmentation. In: Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition, 2097–2104, 2011.Google Scholar
  17. [17]
    Achanta, R.; Shaji, A.; Smith, K.; Lucchi, A.; Fua, P.; S¨usstrunk, S. SLIC superpixels compared to stateof- the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 11, 2274–2282, 2012.CrossRefGoogle Scholar
  18. [18]
    Lloyd, S. P. Least squares quantization in PCM. IEEE Transactions on Information Theory Vol. 28, No. 2, 129–137, 1982.MathSciNetCrossRefzbMATHGoogle Scholar
  19. [19]
    Wang, P.; Zeng, G.; Gan, R.; Wang, J.; Zha, H. Structure-sensitive superpixels via geodesic distance. International Journal of Computer Vision Vol. 103, No. 1, 1–21, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  20. [20]
    Peyr´e, G.; P´echaud, M.; Keriven, R.; Cohen, L. D. Geodesic methods in computer vision and graphics. Foundations and Trends in Computer Graphics and Vision Vol. 5, Nos. 3–4, 197–397, 2010.Google Scholar
  21. [21]
    Liu, Y.-J.; Yu, C.-C.; Yu, M.-J.; He, Y. Manifold SLIC: A fast method to compute content-sensitive superpixels. In: Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition, 651–659, 2016.Google Scholar
  22. [22]
    Du, Q.; Faber, V.; Gunzburger, M. Centroidal Voronoi tessellations: Applications and algorithms. SIAM Review Vol. 41, No. 4, 637–676, 1999.MathSciNetCrossRefzbMATHGoogle Scholar
  23. [23]
    Barnes, C.; Shechtman, E.; Finkelstein, A.; Goldman, D. B. PatchMatch: A randomized correspondence algorithm forstructural image editing. ACM Transactions on Graphics Vol. 28, No. 3, Article No. 24, 2009.Google Scholar
  24. [24]
    Arbelaez, P.; Maire, M.; Fowlkes, C.; Malik, J. Contourdetection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 33, No. 5, 898–916, 2011.CrossRefGoogle Scholar
  25. [25]
    Stutz, D. Superpixelsegmentation: An evaluation. In: Pattern Recognition. Lecture Notes in Computer Science, Vol. 9358. Gall, J.; Gehler, P.; Leibe, B. Eds. Springer Cham, 555–562, 2015.Google Scholar
  26. [26]
    Mottaghi, R.; Chen, X.; Liu, X.; Cho, N.-G.; Lee, S.-W.; Fidler, S.; Urtasun, R.; Yuille, A. The role of contextfor object detection and semantic segmentation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 891–898, 2014.Google Scholar
  27. [27]
    Everingham, M.; Van Gool, L.; Williams, C. K. I.; Winn, J.; Zisserman, A. The PASCAL visual object classes challenge 2010 (VOC2010) results. 2010. Available at https://doi.org/www.pascalnetwork.org/challenges/VOC/voc2010/workshop/index.html.Google Scholar
  28. [28]
    Neubert, P.; Protzel, P. Superpixel benchmark and comparison. In: Proceedings of the Forum Bildverarbeitung, 1–12, 2012.Google Scholar

Copyright information

© The Author(s) 2018

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (https://doi.org/creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from https://doi.org/www.springer.com/journal/41095. To submit a manuscript, please go to https://doi.org/www.editorialmanager.com/cvmj.

Authors and Affiliations

  • Jiaxing Zhao
    • 1
  • Ren Bo
    • 1
  • Qibin Hou
    • 1
  • Ming-Ming Cheng
    • 1
  • Paul Rosin
    • 2
  1. 1.Nankai UniversityTianjinChina
  2. 2.Cardiff UniversityWalesUK

Personalised recommendations