Superpixel segmentation: A benchmark

Signal Processing: Image Communication

Volumn 56, Issue , 2017, Pages 28-39

  Wang, Murong ^a   Liu, Xiabi ^a   Gao, Yixuan ^a   Ma, Xiao ^a   Soomro, Nouman Q ^b  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b MEHRAN UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Pakistan)

Author keywords

Benchmark; Evaluation; Superpixel

Indexed keywords

BENCHMARKING; GRAPHIC METHODS; PIXELS;

ALGORITHM COMPARISON; EVALUATION; EVALUATION METRICS; EVALUATION RESULTS; QUANTITATIVE COMPARISON; STATE-OF-THE-ART ALGORITHMS; SUPER PIXELS; SUPERPIXEL SEGMENTATIONS;

CLUSTERING ALGORITHMS;

EID: 85018251516     PISSN: 09235965     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.image.2017.04.007     Document Type: Article

References (54)

1. [1] Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S., Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34:11 (2012), 2274–2282.
2. [2] X. Ren, J. Malik, Learning a classification model for segmentation, in: Computer Vision, 2003. Proceedings. in: Proceedings of the Ninth IEEE International Conference on, IEEE, 2003, pp. 10–17.
3. [3] Shi, J., Malik, J., Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22:8 (2000), 888–905.
4. [4] Z. Li, J. Chen, Superpixel segmentation using linear spectral clustering, in: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, IEEE, 2015, pp. 1356–1363.
5. [5] Xu, L., Zeng, L., Wang, Z., Saliency-based superpixels. Signal, Image Video Process. 8:1 (2014), 181–190.
6. [6] Fu, H., Cao, X., Tang, D., Han, Y., Xu, D., Regularity preserved superpixels and supervoxels. IEEE Trans. Multimed. 16:4 (2014), 1165–1175.
7. [7] Shen, J., Du, Y., Wang, W., Li, X., Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23:4 (2014), 1451–1462.
8. [8] C. Conrad, M. Mertz, R. Mester, Contour-relaxed superpixels, in: Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer, 2013, pp. 280–293.
9. [9] Wang, P., Zeng, G., Gan, R., Wang, J., Zha, H., Structure-sensitive superpixels via geodesic distance. Int. J. Comput. Vision. 103:1 (2013), 1–21.
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 (2009), 2290–2297.
11. [11] A.P. Moore, J. Prince, J. Warrell, U. Mohammed, G. Jones, Superpixel lattices, in: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, IEEE, 2008, pp. 1–8.
12. [12] O. Veksler, Y. Boykov, P. Mehrani, Superpixels and supervoxels in an energy optimization framework, in: Computer Vision–ECCV 2010, Springer, 2010, pp. 211–224.
13. [13] A.P. Moore, S.J. Prince, J. Warrell, lattice cut-constructing superpixels using layer constraints, in: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE, 2010, pp. 2117–2124.
14. [14] Y. Zhang, R. Hartley, J. Mashford, S. Burn, Superpixels via pseudo-boolean optimization, in: Computer Vision (ICCV), 2011 IEEE International Conference on, IEEE, 2011, pp. 1387–1394.
15. [15] F. Perbet, A. Maki, Homogeneous superpixels from random walks., in: MVA, 2011, pp. 26–30.
16. [16] D. Tang, H. Fu, X. Cao, Topology preserved regular superpixel, in: Multimedia and Expo (ICME), 2012 IEEE International Conference on, IEEE, 2012, pp. 765–768.
17. [17] M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, L. Van Gool, Seeds: Superpixels extracted via energy-driven sampling, in: Computer Vision–ECCV 2012, Springer, 2012, pp. 13–26.
18. [18] D. Weikersdorfer, D. Gossow, M. Beetz, Depth-adaptive superpixels, in: Pattern Recognition (ICPR), 2012 Proceedings of the 21st International Conference on, IEEE, 2012, pp. 2087–2090.
19. [19] Wang, J., Wang, X., Vcells: simple and efficient superpixels using edge-weighted centroidal voronoi tessellations. IEEE Trans. Pattern Anal. Mach. Intell. 34:6 (2012), 1241–1247.
20. [20] J. Papon, A. Abramov, M. Schoeler, F. Worgotter, Voxel cloud connectivity segmentation-supervoxels for point clouds, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2027–2034.
21. [21] M.-Y. Liu, O. Tuzel, S. Ramalingam, R. Chellappa, Entropy rate superpixel segmentation, in: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, 2011, pp. 2097–2104.
22. [22] D. Giordano, F. Murabito, S. Palazzo, C. Spampinato, Superpixel-based video object segmentation using perceptual organization and location prior, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 4814–4822.
23. [23] Arbelaez, P., Maire, M., Fowlkes, C., Malik, J., Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33:5 (2011), 898–916.
24. [24] Yang, F., Lu, H., Yang, M.-H., Robust superpixel tracking. IEEE Trans. Image Process. 23:4 (2014), 1639–1651.
25. [25] Tian, Z., Zheng, N., Xue, J., Lan, X., Li, C., Zhou, G., Video object segmentation with shape cue based on spatiotemporal superpixel neighbourhood. IET Comput. Vision. 8:1 (2014), 16–25.
26. [26] F. Galasso, R. Cipolla, B. Schiele, Video segmentation with superpixels, in: Computer Vision–ACCV 2012, Springer, 2012, pp. 760–774.
27. [27] J. Cheng, J. Liu, Y. Xu, F. Yin, D.W.K. Wong, B.-H. Lee, C. Cheung, T. Aung, T.Y. Wong, Superpixel classification for initialization in model based optic disc segmentation, in: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, IEEE, 2012, pp. 1450–1453.
28. [28] Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D.W.K., Tan, N.-M., Tao, D., Cheng, C.-Y., Aung, T., Wong, T.Y., Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. Med. Imaging 32:6 (2013), 1019–1032.
29. [29] P. Neubert, P. Protzel, Superpixel benchmark and comparison, in: Proceedings Forum Bildverarbeitung, 2012, pp. 1–12.
30. [30] C. Xu, J.J. Corso, Evaluation of super-voxel methods for early video processing, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 1202–1209.
31. [31] Xu, C., Corso, J.J., Libsvx: a supervoxel library and benchmark for early video processing. Int. J. Comput. Vision. 119:3 (2016), 272–290.
32. [32] D. Stutz, Superpixel segmentation: An evaluation, in: Pattern Recognition, Springer, 2015, pp. 555–562.
33. [33] Felzenszwalb, P.F., Huttenlocher, D.P., Efficient graph-based image segmentation. Int. J. Comput. Vision. 59:2 (2004), 167–181.
34. [34] S.M. Van Dongen, Graph clustering by flow simulation.
35. [35] P. Carr, R. Hartley, Minimizing energy functions on 4-connected lattices using elimination, in: 2009 IEEE Proceedings of the 12th International Conference on Computer Vision, IEEE, 2009, pp. 2042–2049.
36. [36] Vincent, L., Soille, P., Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 6 (1991), 583–598.
37. [37] Comaniciu, D., Meer, P., Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24:5 (2002), 603–619.
38. [38] A. Vedaldi, S. Soatto, Quick shift and kernel methods for mode seeking, in: Computer vision–ECCV 2008, Springer, 2008, pp. 705–718.
39. [39] Y.-J. Liu, C.-C. Yu, M.-J. Yu, Y. He, Manifold slic: A fast method to compute content-sensitive superpixels, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 651–659.
40. [40] A.P. Moore, S.J. Prince, J. Warrell, U. Mohammed, G. Jones, Scene shape priors for superpixel segmentation, in: Computer Vision, 2009 IEEE Proceedings of the 12th International Conference on, IEEE, 2009, pp. 771–778.
41. [41] Xie, Y., Xu, L., Wang, Z., Automated co-superpixel generation via graph matching. Signal, Image Video Process. 8:4 (2014), 753–763.
42. [42] P. Neubert, P. Protzel, Compact watershed and preemptive slic: On improving trade-offs of superpixel segmentation algorithms., in: ICPR, 2014, pp. 996–1001.
43. [43] Wang, J., Ju, L., Wang, X., An edge-weighted centroidal voronoi tessellation model for image segmentation. IEEE Trans. Image Process. 18:8 (2009), 1844–1858.
44. [44] R. Fattal, Blue-noise point sampling using kernel density model, in: ACM Transactions on Graphics (TOG), Vol. 30, ACM, 2011, p. 48.
45. [45] Boykov, Y., Funka-Lea, G., Graph cuts and efficient nd image segmentation. Int. J. Comput. Vision. 70:2 (2006), 109–131.
46. [46] R.B. Rusu, N. Blodow, M. Beetz, Fast point feature histograms (fpfh) for 3d registration, in: Robotics and Automation, 2009. ICRA’09. IEEE International Conference on, IEEE, 2009, pp. 3212–3217.
47. [47] A. Goldberg, R. Tarjan, Solving minimum-cost flow problems by successive approximation, in: Proceedings of the nineteenth annual ACM symposium on Theory of computing, ACM, 1987, pp. 7–18.
48. [48] Y.Y. Boykov, M.-P. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in nd images, in: Computer Vision, 2001. ICCV 2001. Proceedings. in: Proceedings of the Eighth IEEE International Conference on, vol. 1, IEEE, 2001, pp. 105–112.
49. [49] C. Rother, V. Kolmogorov, A. Blake, Grabcut: Interactive foreground extraction using iterated graph cuts, in: ACM transactions on graphics (TOG), vol. 23, ACM, 2004, pp. 309–314.
50. [50] Martin, D.R., Fowlkes, C.C., Malik, J., Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26:5 (2004), 530–549.
51. [51] A. Hanbury, How do superpixels affect image segmentation?, in: Iberoamerican Congress on Pattern Recognition, Springer, 2008, pp. 178–186.
52. [52] Rand, W.M., Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66:336 (1971), 846–850.
53. [53] Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34:11 (2012), 2189–2202.
54. [54] C.Y. Ren, I. Reid, gslic: a real-time implementation of slic superpixel segmentation, University of Oxford, Department of Engineering, Technical Report.