Cited By
View all- Barcelos IBelém FJoão LPatrocínio ZFalcão AGuimarães S(2024)A Comprehensive Review and New Taxonomy on Superpixel SegmentationACM Computing Surveys10.1145/365250956:8(1-39)Online publication date: 10-Apr-2024
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.
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
[2]
Achanta R, Shaji A, Smith K, Lucchi A, Fua P, and Süsstrunk S SLIC superpixels compared to state-of-the-art superpixel methods IEEE Trans. Pattern Anal. Mach. Intell. 2012 34 11 2274-2281
[3]
Arbeláez P, Maire M, Fowlkes C, and Malik J Contour detection and hierarchical image segmentation IEEE Trans. Pattern Anal. Mach. Intell. 2011 33 5 898-916
[4]
Drucker, F., MacCormick, J.: Fast superpixels for video analysis. In: WMVC, pp. 1–8. IEEE Computer Society, Snowbird, Utah, December 2009
[5]
Felzenswalb PF and Huttenlocher DP Efficient graph-based image segmentation Int. J. Comput. Vis. 2004 59 2 167-181
[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
[7]
Levinshtein A, Stere A, Kutulakos KN, Fleet DJ, Dickinson SJ, and Siddiqi K TurboPixels: fast superpixels using geometric flows IEEE Trans. Pattern Anal. Mach. Intell. 2009 31 12 2290-2297
[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
[9]
Martin D, Fowlkes C, and Malik J Learning to detect natural image boundaries using local brightness, color, and texture cues IEEE Trans. Pattern Anal. Mach. Intell. 2004 26 5 530-549
[10]
Mester R, Conrad C, and Guevara A Heyden A and Kahl F Multichannel segmentation using contour relaxation: fast super-pixels and temporal propagation Image Analysis 2011 Heidelberg Springer 250-261
[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
[12]
Neubert, P., Protzel, P.: Superpixel benchmark and comparison. In: Forum Bildverarbeitung, Regensburg, Germany, November 2012
[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
[14]
Perbet, F., Maki, A.: Homogeneous superpixels from random walks. In: (IAPR) MVA, pp. 26–30. Nara, Japan, June 2011
[15]
Ren X and Bo L Bartlett PL, Pereira FCN, Burges CJC, Bottou L, and Weinberger KQ Discriminatively trained sparse code gradients for contour detection NIPS 2012 Red Hook, New York Curran Associates 584-592
[16]
Ren, X., Malik, J.: Learning a classification model for segmentation. In: ICCV, pp. 10–17. IEEE Computer Society, Nice, France, October 2003
[17]
Rohkohl C and Engel K Hamprecht FA, Schnörr C, and Jähne B Efficient image segmentation using pairwise pixel similarities Pattern Recognition 2007 Heidelberg Springer 254-263
[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
[19]
Silberman N, Hoiem D, Kohli P, and Fergus R Fitzgibbon A, Lazebnik S, Perona P, Sato Y, and Schmid C Indoor segmentation and support inference from RGBD images Computer Vision – ECCV 2012 2012 Heidelberg Springer 746-760
[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
[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
[23]
Van den Bergh M, Boix X, Roig G, de Capitani B, and Van Gool L Fitzgibbon A, Lazebnik S, Perona P, Sato Y, and Schmid C SEEDS: superpixels extracted via energy-driven sampling Computer Vision – ECCV 2012 2012 Heidelberg Springer 13-26
[24]
Vedaldi A and Soatto S Forsyth D, Torr P, and Zisserman A Quick shift and Kernel methods for mode seeking Computer Vision – ECCV 2008 2008 Heidelberg Springer 705-718
[25]
Veksler O, Boykov Y, and Mehrani P Daniilidis K, Maragos P, and Paragios N Superpixels and supervoxels in an energy optimization framework Computer Vision – ECCV 2010 2010 Heidelberg Springer 211-224
[26]
Wang, S., Lu, H., Yang, F., Yang, M.H.: Superpixel tracking. In: ICCV, pp. 1323–1330. IEEE Computer Society, Barcelona, Spain, November 2011
[27]
Weikersdorfer, D., Gossow, D., Beetz, M.: Depth-adaptive superpixels. In: ICPR. pp. 2087–2090. IEEE Computer Society, Tsukuba, Japan, November 2012
[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
[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
[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
Index Terms
- Superpixel Segmentation: An Evaluation
Index terms have been assigned to the content through auto-classification.
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Oct 2015
553 pages
© Springer International Publishing Switzerland 2015.
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Springer-Verlag
Berlin, Heidelberg
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Published: 03 November 2015
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View all- Barcelos IBelém FJoão LPatrocínio ZFalcão AGuimarães S(2024)A Comprehensive Review and New Taxonomy on Superpixel SegmentationACM Computing Surveys10.1145/365250956:8(1-39)Online publication date: 10-Apr-2024