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Superpixel Segmentation: An Evaluation

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

In recent years, superpixel algorithms have become a standard tool in computer vision and many approaches have been proposed. However, different evaluation methodologies make direct comparison difficult. We address this shortcoming with a thorough and fair comparison of thirteen state-of-the-art superpixel algorithms. To include algorithms utilizing depth information we present results on both the Berkeley Segmentation Dataset [3] and the NYU Depth Dataset [19]. Based on qualitative and quantitative aspects, our work allows to guide algorithm selection by identifying important quality characteristics.

Recommended for submission to YRF2015 by Prof. Dr. Bastian Leibe.

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References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. Technical report, École Polytechnique Fédérale de Lausanne, Lusanne, Switzerland, June 2010

    Google Scholar 

  2. 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), 2274–2281 (2012)

    Article  Google Scholar 

  3. Arbeláez, 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 

  4. Drucker, F., MacCormick, J.: Fast superpixels for video analysis. In: WMVC, pp. 1–8. IEEE Computer Society, Snowbird, Utah, December 2009

    Google Scholar 

  5. Felzenswalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)

    Article  Google Scholar 

  6. Gupta, S., Arbeláez, P., Malik, J.: Perceptual organization and recognition of indoor scenes from RGB-D images. In: CVPR, pp. 564–571. IEEE Computer Society, Portland, Oregon, June 2013

    Google Scholar 

  7. 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 

  8. Lui, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: CVPR, pp. 2097–2104. IEEE Computer Society, Providence, RI, June 2011

    Google Scholar 

  9. Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)

    Article  Google Scholar 

  10. Mester, R., Conrad, C., Guevara, A.: Multichannel segmentation using contour relaxation: fast super-pixels and temporal propagation. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 250–261. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices. In: CVPR, pp. 1–8. IEEE Computer Society, Anchorage, AK, June 2008

    Google Scholar 

  12. Neubert, P., Protzel, P.: Superpixel benchmark and comparison. In: Forum Bildverarbeitung, Regensburg, Germany, November 2012

    Google Scholar 

  13. Papon, J., Abramov, A., Schoeler, M., Wörgötter, F.: Voxel cloud connectivity segmentation - supervoxels for point clouds. In: CVPR, pp. 2027–2034. IEEE Computer Society, Portland, Oregon, June 2013

    Google Scholar 

  14. Perbet, F., Maki, A.: Homogeneous superpixels from random walks. In: (IAPR) MVA, pp. 26–30. Nara, Japan, June 2011

    Google Scholar 

  15. Ren, X., Bo, L.: Discriminatively trained sparse code gradients for contour detection. In: Bartlett, P.L., Pereira, F.C.N., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) NIPS, vol. 25, pp. 584–592. Curran Associates, Red Hook, New York (2012)

    Google Scholar 

  16. Ren, X., Malik, J.: Learning a classification model for segmentation. In: ICCV, pp. 10–17. IEEE Computer Society, Nice, France, October 2003

    Google Scholar 

  17. Rohkohl, C., Engel, K.: Efficient image segmentation using pairwise pixel similarities. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 254–263. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Schick, A., Fischer, M., Stiefelhagen, R.: Measuring and evaluating the compactness of superpixels. In: ICPR, pp. 930–934. IEEE Computer Society, Tsukuba, Japan, November 2012

    Google Scholar 

  19. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  20. Siva, P., Wong, A.: Grid seams: A fast superpixel algorithm for real-time applications. In: CRV, pp. 127–134. IEEE Computer Society, Montreal, Canada, May 2014

    Google Scholar 

  21. Stutz, D.: Superpixel segmentation using depth information. B.Sc. thesis, Computer Vision Group, RWTH Aachen University, Aachen, Germany, September 2014. http://davidstutz.de/projects/superpixelsseeds/

  22. Tang, D., Fu, H., Cao, X.: Topology preserved regular superpixel. In: ICME, pp. 765–768. IEEE Computer Society, Melbourne, Australia, July 2012

    Google Scholar 

  23. Van den Bergh, M., Boix, X., Roig, G., de Capitani, B., Van Gool, L.: SEEDS: superpixels extracted via energy-driven sampling. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 13–26. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  24. Vedaldi, A., Soatto, S.: Quick shift and Kernel methods for mode seeking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 705–718. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  25. Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 211–224. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  26. Wang, S., Lu, H., Yang, F., Yang, M.H.: Superpixel tracking. In: ICCV, pp. 1323–1330. IEEE Computer Society, Barcelona, Spain, November 2011

    Google Scholar 

  27. Weikersdorfer, D., Gossow, D., Beetz, M.: Depth-adaptive superpixels. In: ICPR. pp. 2087–2090. IEEE Computer Society, Tsukuba, Japan, November 2012

    Google Scholar 

  28. Zeng, G., Wang, P., Wang, J., Gan, R., Zha, H.: Structure-sensitive superpixels via geodesic distance. In: ICCV, pp. 447–454. IEEE Computer Society, Barcelona, Spain, November 2011

    Google Scholar 

  29. Zhang, Y., Hartley, R., Mashford, J., Burn, S.: Superpixels via pseudo-boolean optimization. In: ICCV, pp. 1387–1394. IEEE Computer Society, Barcelona, Spain, November 2011

    Google Scholar 

  30. Zhang, Y., Hartley, R., Mashford, J., Burn, S.: Superpixels, occlusion and stereo. In: DICTA, pp. 84–91. IEEE Computer Society, Noosa, Australia, December 2011

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Vision Group, RWTH Aachen University, Aachen, Germany

    David Stutz

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  1. David Stutz
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Correspondence to David Stutz .

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Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Stutz, D. (2015). Superpixel Segmentation: An Evaluation. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_46

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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