[1] Ren X, Malik J. Learning a classification model for segmentation. [C]∥Proceedings of the Ninth IEEE International Conference on Computer Vision, 10-17(2003).

[2] Song X Y, Zhou L L, Li Z G et al. Review on superpixel methods in image segmentation[J]. Journal of Image and Graphics, 20, 599-608(2015).

[3] Wang C Y, Chen J Z, Li W. Review on superpixel segmentation algorithms[J]. Application Research of Computers, 31, 6-12(2014).

[4] Gong Y J, Zhou Y C, Zhang X L. A superpixel segmentation algorithm based on differential evolution. [C]∥2016 IEEE International Conference on Multimedia and Expo (ICME), 1-6(2016).

[5] Gong Y J, Zhou Y C. Differential evolutionary superpixel segmentation[J]. IEEE Transactions on Image Processing, 27, 1390-1404(2018).

[6] Nakamura K, Hong B W. Fast-convergence superpixel algorithm via an approximate optimization[J]. Journal of Electronic Imaging, 25, 053035(2016). http://proceedings.spiedigitallibrary.org/article.aspx?articleid=2575786&resultClick=1

[7] Peng J T, Shen J B, Yao A et al. Superpixel optimization using higher order energy[J]. IEEE Transactions on Circuits and Systems for Video Technology, 26, 917-927(2016). http://ieeexplore.ieee.org/document/7103037/

[8] Chen J S, Li Z Q, Huang B. Linear spectral clustering superpixel[J]. IEEE Transactions on Image Processing, 26, 3317-3330(2017).

[9] Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012). http://europepmc.org/abstract/med/22641706

[10] Shen J B, Hao X P, Liang Z Y et al. Real-time superpixel segmentation by DBSCAN clustering algorithm[J]. IEEE Transactions on Image Processing, 25, 5933-5942(2016). http://www.ncbi.nlm.nih.gov/pubmed/27740485

[11] Zhao J, Bo R, Hou Q et al. FLIC: fast linear iterative clustering with active search[J]. Computational Visual Media, 4, 333-348(2018).

[12] Xiao X L, Zhou Y C, Gong Y J. Content-adaptive superpixel segmentation[J]. IEEE Transactions on Image Processing, 27, 2883-2896(2018).

[13] Ban Z H, Liu J G, Cao L. Superpixel segmentation using gaussian mixture model[J]. IEEE Transactions on Image Processing, 27, 4105-4117(2018). http://ieeexplore.ieee.org/document/8360143/

[14] Hu Y L, Li Y S, Song R et al. Minimum barrier superpixel segmentation[J]. Image and Vision Computing, 70, 1-10(2018).

[15] Wang P, Zeng G, Gan R et al. Structure-sensitive superpixels via geodesic distance[J]. International Journal of Computer Vision, 103, 1-21(2013). http://ieeeexplore.com/xpl/articleDetails.jsp?tp=&arnumber=6126274&searchWithin%3Dp_Authors%3A.QT.Gang+Zeng.QT.%26pageNumber%3D3

[16] Giraud R, Ta V T, Papadakis N. Robust superpixels using color and contour features along linear path[J]. Computer Vision and Image Understanding, 170, 1-13(2018). http://www.sciencedirect.com/science/article/pii/S1077314218300067

[17] Fang Z Q, Yu X S, Wu C D et al. Superpixel segmentation using weighted coplanar feature clustering on RGBD images[J]. Applied Sciences, 8, 902(2018).

[18] Zhang Y X, Li X M, Gao X F et al. A simple algorithm of superpixel segmentation with boundary constraint[J]. IEEE Transactions on Circuits and Systems for Video Technology, 27, 1502-1514(2017). http://ieeexplore.ieee.org/document/7428902/

[19] Huang C R, Wang W C, Wang W A et al. USEAQ: ultra-fast superpixel extraction via adaptive sampling from quantized regions[J]. IEEE Transactions on Image Processing, 27, 4916-4931(2018). http://ieeexplore.ieee.org/document/8387791/

[20] Achanta R, Süsstrunk S. Superpixels and polygons using simple non-iterative clustering. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4895-4904(2017).

[21] Bai Y, Chen X J. Efficient structure-preserving superpixel segmentation based on minimum spanning tree. [C]∥2016 IEEE International Conference on Multimedia and Expo (ICME), 1-6(2016).

[22] Chaibou M S, Conze P H, Kalti K et al. Adaptive strategy for superpixel-based region-growing image segmentation[J]. Journal of Electronic Imaging, 26, 061605(2017). http://arxiv.org/abs/1803.06541

[23] Chai D F, Huang Y T, Bao Y J. IRSL: iterative refining superpixel lattice[J]. IEEE Geoscience and Remote Sensing Letters, 14, 344-348(2017). http://ieeexplore.ieee.org/document/7811299/

[24] Moore A P. Prince S J D, Warrell J, et al. Superpixel lattices. [C]∥2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8(2008).

[25] Zhao Q, Wan L, Zhang J W. Spherical superpixel segmentation. [C]∥2016 IEEE International Conference on Multimedia and Expo (ICME), 1-6(2016).

[26] Zhao Q, Dai F, Ma Y K et al. Spherical superpixel segmentation[J]. IEEE Transactions on Multimedia, 20, 1406-1417(2018).

[27] Amami A, Azouz Z B. Alouane M T H. AdaSLIC: adaptive supervoxel generation for volumetric medical images[J]. Multimedia Tools and Applications, 78, 3723-3745(2019). http://link.springer.com/10.1007/s11042-017-5563-3

[28] Zhang Y X, Ma L, Zhou Y F et al. Automatic superpixel generation algorithm based on a quadric error metric in 3D space[J]. Signal, Image and Video Processing, 11, 471-478(2017). http://link.springer.com/10.1007/s11760-016-0983-5

[29] Cai Y Q, Guo X H. Anisotropic superpixel generation based on mahalanobis distance[J]. Computer Graphics Forum, 35, 199-207(2016). http://onlinelibrary.wiley.com/doi/10.1111/cgf.13017/pdf

[30] Su T F, Zhang S W, Li H Y. High resolution remote sensing image super-pixel segmentation based on seeded region growing[J]. Science of Surveying and Mapping, 43, 122-129(2018).

[31] Wang W, Xiang D L, Ban Y F et al. Superpixel segmentation of polarimetric SAR images based on integrated distance measure and entropy rate method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4045-4058(2017). http://ieeexplore.ieee.org/document/7942039/

[32] Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 59, 167-181(2004). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1023/B:VISI.0000022288.19776.77&rfr_id=trans/tp/2008/02/ttp2008020299.htm

[33] Levinshtein A, Stere A, Kutulakos K N et al. TurboPixels: fast superpixels using geometric flows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 2290-2297(2009). http://europepmc.org/abstract/med/19834148

[34] Vedaldi A, Soatto S. Quick shift and kernel methods for mode seeking[M]. ∥Vedaldi A, Soatto S. eds. Heidelberg: Springer, 705-718(2008).

[35] Stutz D[M]. Superpixel segmentation: an evaluation, 555-562(2015).

[36] Mathieu B, Crouzil A, Puel J B[M]. Over segmentation methods: a new evaluation, 185-193(2017).

[37] Liu M, Tuzel O, Ramalingam S et al. Entropy rate superpixel segmentation. [C] ∥Proceedings of Computer Vision and Pattern Recognition, 2097-2104(2011).

[38] Zhang Y H, Hartley R, Mashford J et al. Superpixels via pseudo-Boolean optimization. [C]∥2011 International Conference on Computer Vision, 1387-1394(2011).

[39] Conrad C, Mertz M, Mester R. Contour-relaxed superpixels[M]. ∥Conrad C, Mertz M, Mester R. Heidelberg: Springer, 280-293(2013).

[40] Tang D, Fu H Z, Cao X C. Topology preserved regular superpixel. [C]∥2012 IEEE International Conference on Multimedia and Expo, 765-768(2012).

[41] Stutz D, Hermans A, Leibe B. Superpixels: an evaluation of the state-of-the-art[J]. Computer Vision and Image Understanding, 166, 1-27(2018). http://www.sciencedirect.com/science/article/pii/S1077314217300589

[42] Gould S, Fulton R, Koller D. Decomposing a scene into geometric and semantically consistent regions. [C]∥2009 IEEE 12th International Conference on Computer Vision, 1-8(2009).

[43] Song S R, Lichtenberg S P, Xiao J X. SUN RGB-D: a RGB-D scene understanding benchmark suite. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 567-576(2015).

[44] Yamaguchi K, Kiapour M H, Ortiz L E et al. Parsing clothing in fashion photographs. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, 3570-3577(2012).

[45] Giraud R, Ta V T, Papadakis N. Robust shape regularity criteria for superpixel evaluation. [C]∥IEEE International Conference on Image Processing, 3455-3459(2017).

[46] Giraud R, Ta V T, Papadakis N. Evaluation framework of superpixel methods with a global regularity measure[J]. Journal of Electronic Imaging, 26, 061603(2017). http://arxiv.org/abs/1903.07162?context=cs

[47] Brekhna B, Mahmood A, Zhou Y F. Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise[J]. Journal of Electronic Imaging, 26, 061604(2017). http://adsabs.harvard.edu/abs/2017JEI....26f1604B

[48] Wang C M, Yang X Z, Zhou F. Research on anti-noise performance of superpixel segmentation algorithms of SAR image[J]. Journal of Hefei University of Technology (Natural Science), 39, 1626-1632(2016).

[49] Zhang L G, Verma B, Stockwell D. Spatial contextual superpixel model for natural roadside vegetation classification[J]. Pattern Recognition, 60, 444-457(2016). http://www.sciencedirect.com/science/article/pii/S0031320316300966

[50] Arisoy S, Kayabol K. Mixture-based superpixel segmentation and classification of SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 13, 1721-1725(2016). http://ieeexplore.ieee.org/document/7572009/

[51] [51] FanF, MaY, LiC, et al. Hyperspectral image denoising with superpixel segmentation and low-rank representation[J]. InformationSciences, 2017, 397/398: 48-68.

[52] Jin X D, Gu Y F. Superpixel-based intrinsic image decomposition of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 4285-4295(2017). http://ieeexplore.ieee.org/document/7913613/

[53] Pappas O, Achim A, Bull D. Superpixel-level CFAR detectors for ship detection in SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 15, 1397-1401(2018). http://ieeexplore.ieee.org/document/8392373

[54] Li T, Liu Z, Xie R et al. An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 184-194(2018). http://ieeexplore.ieee.org/document/8094932/

[55] Giraud R, Ta V T, Bugeau A et al. SuperPatchMatch: an algorithm for robust correspondences using superpixel patches[J]. IEEE Transactions on Image Processing, 26, 4068-4078(2017). http://www.ncbi.nlm.nih.gov/pubmed/28574354

[56] Jiang J J, Ma J Y, Chen C et al. SuperPCA: a superpixel wise PCA approach for unsupervised feature extraction of hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 4581-4593(2018). http://ieeexplore.ieee.org/document/8390924/

[57] Sun W, Liao Q M, Xue J H et al. SPSIM: a superpixel-based similarity index for full-reference image quality assessment[J]. IEEE Transactions on Image Processing, 27, 4232-4244(2018). http://ieeexplore.ieee.org/document/8360170/

[58] Boemer F, Ratner E, Lendasse A. Parameter-free image segmentation with SLIC[J]. Neurocomputing, 277, 228-236(2018). http://www.sciencedirect.com/science/article/pii/S0925231217313978

[59] Gangapure V N, Nanda S, Chowdhury A S. Superpixel-based causal multisensor video fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 28, 1263-1272(2018). http://ieeexplore.ieee.org/document/7839950/

[60] Yan Q, Li H, Jing L H et al. An extraction algorithm of remote sensing information based on similarity measurement for superpixel regions[J]. Laser & Optoelectronics Progress, 54, 081004(2017).

[61] Guo C C, Yu F Q, Chen Y. Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J]. Laser & Optoelectronics Progress, 55, 081005(2018).