Algorithms

An overview of the evaluated algorithms can be found below. More details can be found in the paper or in the original README found in the respective library directory. Also check the corresponding web pages for author and license information. The corresponding references are given below the table.

Algorithm	Library	Executable	Implementation	License	Reference	Link
CCS	lib_ccs	ccs_cli	C++	?	[22,23]	Web (link remove, please check paper)
CIS	lib_cis	cis_cli	C++	non-comm.	[10]	Web
CRS	lib_crs	crs_cli	C++	GPL3	[13,14]	Web
CW	lib_cw	cw_cli	C++	GPL3	[25]	Web
DASP	lib_dasp	dasp_cli	C++	BSD3	[17]	Web
EAMS	lib_eams	eams_cli	MatLab	?	[2]	Web
ERS	lib_ers	ers_cli	C++	MIT	[15]	Web
FH	lib_fh	fh_cli	C++	GPL2	[4]	Web
reFH	lib_refh	refh_cli	C++	BSD3	--	Web
MSS	lib_mss	mss_cli	C++	BSD3	[28]	--
PB	lib_pb	pb_cli	C++	non-comm.	[16]	Web
preSLIC	lib_preslic	preslic_cli	C++	GPL3	[25]	Web
SEEDS	lib_seeds	seeds_cli	C++	GPL3	[18]	Web
reSEEDS	lib_reseeds	reseeds_cli	C++	BSD3	--	Web
SEAW	lib_seaw	seaw_cli	MatLab	?	[34]	Web
SLIC	lib_slic	slic_cli	C++	GPL3	[11,12]	Web
vlSLIC	lib_vlslic	vlslic_cli	C++	BSD2	--	Web
TP	lib_tp	tp_cli	MatLab	?	[9]	Web
TPS	lib_tps	tps_cli	MatLab	?	[19,20]	Web
W	lib_w	w_cli	C++		[1]	Web
WP	lib_wp	wp_cli	Python	?	[29,30]	Web
PF	lib_pf	pf_cli	Java	?	[8]	Web
LSC	lib_lsc	lsc_cli	C++	?	[32]	Web
RW	lib_rw	rw_cli	MatLab	? + GPL2	[5, 6]	Web
QS	lib_qs	qs_cli	MatLab	BSD2	[7]	Web
NC	lib_nc	nc_cli	Matlab	?	[3]	Web
VCCS	lib_vccs	vccs_cli	C++	BSD3	[24]	Web
POISE	lib_poise	poise_cli	MatLab	?	[33]	Web
VC	lib_vc	vc_cli	C++	non-comm.	[21]	Web
ETPS	lib_etps	etps_cli	C++	non-comm.	[31]	Web
ERGC	lib_ergc	ergc_cli	C++	?	[26,27]	Web

[1] F. Meyer.
    Color image segmentation.
    International Conference on Image Processing and its Applications, 1992, pp. 303-306.
[2] D. Comaniciu, P. Meer.
    Mean shift: A robust approach toward feature space analysis.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (5) (2002) 603–619.
[3] X. Ren, J. Malik.
    Learning a classification model for segmentation.
    International Conference on Computer Vision, 2003, pp. 10–17.
[4] P. F. Felzenswalb, D. P. Huttenlocher.
    Efficient graph-based image segmentation.
    International Journal of Computer Vision 59 (2) (2004) 167–181.
[5] L. Grady, G. Funka-Lea.
    Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials.
    ECCV Workshops on Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, 2004, pp. 230–245.
[6] L. Grady.
    Random walks for image segmentation.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (11) (2006) 1768–1783.
[7] A. Vedaldi, S. Soatto.
    Quick shift and kernel methods for mode seeking.
    European Conference on Computer Vision, Vol. 5305, 2008, pp. 705–718.
[8] F. Drucker, J. MacCormick.
    Fast superpixels for video analysis.
    Workshop on Motion and Video Computing, 2009, pp. 1–8.
[9] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Siddiqi.
    TurboPixels: Fast superpixels using geometric flows.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12) (2009) 2290–2297.
[10] O. Veksler, Y. Boykov, P. Mehrani.
     Superpixels and supervoxels in an energy optimization framework.
     European Conference on Computer Vision, Vol. 6315, 2010, pp. 211–224.
[11] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk.
     SLIC superpixels.
     Tech. rep., Ecole Polytechnique Federale de Lausanne (2010).
[12] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk.
     SLIC superpixels compared to state-of-the-art superpixel methods.
     IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11) (2012) 2274–2281.
[13] R. Mester, C. Conrad, A. Guevara.
     Multichannel segmentation using contour relaxation: Fast super-pixels and temporal propagation.
     Scandinavian Conference Image Analysis, 2011, pp. 250–261.
[14] C. Conrad, M. Mertz, R. Mester, Contour-relaxed superpixels. 
     Energy Minimization Methods.
     Computer Vision and Pattern Recognition, 2013, pp. 280–293.
[15] M. Y. Lui, O. Tuzel, S. Ramalingam, R. Chellappa.
     Entropy rate superpixel segmentation.
     IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 2097–2104.
[16] Y. Zhang, R. Hartley, J. Mashford, S. Burn.
     Superpixels via pseudo-boolean optimization.
     International Conference on Computer Vision, 2011, pp. 1387–1394.
[17] D. Weikersdorfer, D. Gossow, M. Beetz.
     Depth-adaptive superpixels.
     International Conference on Pattern Recognition, 2012, pp. 2087–2090.
[18] M. van den Bergh, X. Boix, G. Roig, B. de Capitani, L. van Gool.
     SEEDS: Superpixels extracted via energy-driven sampling.
     European Conference on Computer Vision, Vol. 7578, 2012, pp. 13–26.
[19] D. Tang, H. Fu, X. Cao.
     Topology preserved regular superpixel.
     IEEE International Conference on Multimedia and Expo, 2012, pp. 765–768.
[20] H. Fu, X. Cao, D. Tang, Y. Han, D. Xu.
     Regularity preserved superpixels and supervoxels.
     IEEE Transactions on Multimedia 16 (4) (2014) 1165–1175.
[21] J. Wang, X. Wang.
     VCells: Simple and efficient superpixels using edge-weighted centroidal voronoi tessellations.
     IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (6)(2012) 1241–1247.
[22] H. E. Tasli, C. Cigla, T. Gevers, A. A. Alatan.
     Super pixel extraction via convexity induced boundary adaptation.
     IEEE International Conference on Multimedia and Expo, 2013, pp. 1–6.
[23] H. E. Tasli, C. Cigla, A. A. Alatan.
     Convexity constrained efficient superpixel and supervoxel extraction.
     Signal Processing: Image Communication 33 (2015) 71–85.
[24] J. Papon, A. Abramov, M. Schoeler, F. Wörgötter.
     Voxel cloud connectivity segmentation - supervoxels for point clouds.
     IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2027–2034.
[25] P. Neubert, P. Protzel.
     Compact watershed and preemptive SLIC: on improving trade-offs of superpixel segmentation algorithms.
     International Conference on Pattern Recognition, 2014, pp. 996–1001.
[26] P. Buyssens, I. Gardin, S. Ruan.
     Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images.
     Innovation and Research in BioMedical Engineering 35 (1) (2014) 20–26.
[27] P. Buyssens, M. Toutain, A. Elmoataz, O. Lézoray.
     Eikonal-based vertices growing and iterative seeding for efficient graph-based segmentation.
     International Conference on Image Processing, 2014, pp. 4368–4372
[28] W. Benesova, M. Kottman.
     Fast superpixel segmentation using morphological processing.
     Conference on Machine Vision and Machine Learning, 2014.
[29] V. Machairas, E. Decencière, T. Walter.
     Waterpixels: Superpixels based on the watershed transformation.
     International Conference on Image Processing, 2014, pp. 4343–4347.
[30] V. Machairas, M. Faessel, D. Cardenas-Pena, T. Chabardes, T. Walter, E. Decencière.
     Waterpixels.
     Transactions on Image Processing 24 (11) (2015) 3707–3716.
[31] J. Yao, M. Boben, S. Fidler, R. Urtasun.
     Real-time coarse-to-fine topologically preserving segmentation.
     IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2947–2955.
[32] Z. Li, J. Chen.
     Superpixel segmentation using linear spectral clustering.
     IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1356–1363.
[33] J. M. R. A. Humayun, F. Li.
     The middle child problem: Revisiting parametric min-cut and seeds for object proposals.
     International Conference on Computer Vision, 2015, pp. 1600–1608.
[34] J. Strassburg, R. Grzeszick, L. Rothacker, G. A. Fink.
     On the influence of superpixel methods for image parsing.
     International Conference on Computer Vision Theory and Application, 2015, pp. 518–527.