A Comprehensive Review and New Taxonomy on Superpixel Segmentation
Authors: 
Isabela Borlido Barcelos, Felipe De Castro Belém, Leonardo De Melo João, Zenilton K. G. Do Patrocínio, Jr., Alexandre Xavier Falcão, and Silvio Jamil Ferzoli GuimarãesAuthors Info & Claims
Isabela Borlido Barcelos, Felipe De Castro Belém, Leonardo De Melo João, Zenilton K. G. Do Patrocínio, Jr., Alexandre Xavier Falcão, and Silvio Jamil Ferzoli GuimarãesAuthors Info & ClaimsArticle No.: 200, Pages 1 - 39
Abstract
Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications, since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among the compared ones and to categorize the methods according to all existing strategies. This work fills this gap by presenting a comprehensive review with a new taxonomy for superpixel segmentation, in which methods are classified according to their processing steps and processing levels of image features. We revisit the recent and popular literature according to our taxonomy and evaluate 23 strategies and a grid segmentation baseline based on nine criteria: connectivity, compactness, delineation, control over the number of superpixels, color homogeneity, robustness, running time, stability, and visual quality. Our experiments show the trends of each approach in superpixel segmentation and discuss individual trade-offs. Finally, we provide a new benchmark for superpixel assessment, available at https://github.com/IMScience-PPGINF-PucMinas/superpixel-benchmark.
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References
[1]
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 11 (2012), 2274–2282.
[2]
Radhakrishna Achanta and Sabine Süsstrunk. 2017. Superpixels and polygons using simple non-iterative clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 4895–4904.
[3]
Eduardo Barreto Alexandre, Ananda Shankar Chowdhury, Alexandre Xavier Falcao, and Paulo A. Vechiatto Miranda. 2015. IFT-SLIC: A general framework for superpixel generation based on simple linear iterative clustering and image foresting transform. In Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images. 337–344.
[4]
Jianqiao An, Yucheng Shi, Yahong Han, Meijun Sun, and Qi Tian. 2020. Extract and merge: Superpixel segmentation with regional attributes. In Proceedings of the European Conference on Computer Vision. Springer, 155–170.
[5]
Zhihua Ban, Jianguo Liu, and Li Cao. 2018. Superpixel segmentation using gaussian mixture model. IEEE Trans. Image Process. 27, 8 (2018), 4105–4117.
[6]
Zhihua Ban, Jianguo Liu, and Jeremy Fouriaux. 2020. GMMSP on GPU. J. Real-Time Image Process. 17, 2 (2020), 245–257.
[7]
Isabela B. Barcelos, Felipe C. Belém, Leonardo de Melo João, Alexandre X. Falcão, and Silvio J. F. Guimarães. 2022. Improving color homogeneity measure in superpixel segmentation assessment. In Proceedings of the 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI’22), Vol. 1. 79–84.
[8]
Hans H. C. Bejar, Silvio Jamil Ferzoli Guimarães, and Paulo A. V. Miranda. 2020. Efficient hierarchical graph partitioning for image segmentation by optimum oriented cuts. Pattern Recogn. Lett. 131 (2020), 185–192.
[9]
Felipe Belém, Isabela Borlido, Leonardo João, Benjamin Perret, Jean Cousty, Silvio J. F. Guimarães, and Alexandre Falcão. 2022. Fast and effective superpixel segmentation using accurate saliency estimation. In Proceedings of the International Conference on Discrete Geometry and Mathematical Morphology. Springer, 261–273.
[10]
Felipe Belém, Silvio Jamil F. Guimarães, and Alexandre Xavier Falcão. 2018. Superpixel segmentation by object-based iterative spanning forest. In Proceedings of the Iberoamerican Congress on Pattern Recognition. Springer, 334–341.
[11]
Felipe Belém, Benjamin Perret, Jean Cousty, Silvio J. F. Guimarães, and Alexandre Falcão. 2022. Efficient multiscale object-based superpixel framework. Retrieved from https://arXiv:2204.03533
[12]
Felipe C. Belém, Silvio Jamil F. Guimarães, and Alexandre X. Falcão. 2020. Superpixel segmentation using dynamic and iterative spanning forest. IEEE Signal Process. Lett. 27 (2020), 1440–1444.
[13]
Felipe C. Belém, Benjamin Perret, Jean Cousty, Silvio J. F. Guimarães, and Alexandre X. Falcão. 2021. Towards a simple and efficient object-based superpixel delineation framework. In Proceedings of the 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI’21). IEEE, 346–353.
[14]
Serge Bobbia, Richard Macwan, Yannick Benezeth, Keisuke Nakamura, Randy Gomez, and Julien Dubois. 2021. Iterative boundaries implicit identification for superpixels segmentation: A real-time approach. IEEE Access 9 (2021), 77250–77263.
[15]
Isabela Borlido Barcelos, Felipe Belém, Paulo Miranda, Alexandre Xavier Falcão, Zenilton K. G. do Patrocínio, and Silvio Jamil F. Guimarães. 2021. Towards interactive image segmentation by dynamic and iterative spanning forest. In Proceedings of the International Conference on Discrete Geometry and Mathematical Morphology. Springer, 351–364.
[16]
Pierre Buyssens, Isabelle Gardin, and Su Ruan. 2014. Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images. IRBM 35, 1 (2014), 20–26.
[17]
Dengfeng Chai. 2020. Rooted spanning superpixels. Int. J. Comput. Vision 128, 12 (2020), 2962–2978.
[18]
Jiansheng Chen, Zhengqin Li, and Bo Huang. 2017. Linear spectral clustering superpixel. IEEE Trans. Image Process. 26, 7 (2017), 3317–3330.
[19]
Christian Conrad, Matthias Mertz, and Rudolf Mester. 2013. Contour-relaxed superpixels. In Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer, Berlin, 280–293.
[20]
Shuanhu Di, Miao Liao, Yuqian Zhao, Yang Li, and Yezhan Zeng. 2021. Image superpixel segmentation based on hierarchical multi-level LI-SLIC. Optics Laser Technol. 135 (2021), 106703.
[21]
Moshe Eliasof, Nir Ben Zikri, and Eran Treister. 2022. Rethinking unsupervised neural superpixel segmentation. In Proceedings of the IEEE International Conference on Image Processing (ICIP’22). IEEE, 3500–3504.
[22]
Alexandre X. Falcão, Jorge Stolfi, and Roberto de Alencar Lotufo. 2004. The image foresting transform: Theory, algorithms, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1 (2004), 19–29.
[23]
Leyuan Fang, Shutao Li, Xudong Kang, and Jon Atli Benediktsson. 2015. Spectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model. IEEE Trans. Geosci. Remote Sens. 53, 8 (2015), 4186–4201.
[24]
Jobin Francis, M. Baburaj, and Sudhish N. George. 2022. An \(l_{1/2}\) and graph regularized subspace clustering method for robust image segmentation. ACM Trans. Multimedia Comput., Commun. Appl. 18, 2 (2022), 1–24.
[25]
Felipe Lemes Galvão, Alexandre Xavier Falcão, and Ananda Shankar Chowdhury. 2018. RISF: Recursive iterative spanning forest for superpixel segmentation. In Proceedings of the 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI’18). IEEE, 408–415.
[26]
Felipe Lemes Galvão, Silvio Jamil Ferzoli Guimarães, and Alexandre Xavier Falcão. 2020. Image segmentation using dense and sparse hierarchies of superpixels. Pattern Recogn. 108 (2020), 107532.
[27]
Utkarsh Gaur and B. S. Manjunath. 2020. Superpixel embedding network. IEEE Trans. Image Process. 29 (2020), 3199–3212.
[28]
Rémi Giraud, Vinh-Thong Ta, and Nicolas Papadakis. 2018. Robust superpixels using color and contour features along linear path. Comput. Vision Image Understand. 170 (2018), 1–13.
[29]
Jianglei Gong, Nannan Liao, Cheng Li, Xiaojun Ma, Wangpeng He, and Baolong Guo. 2021. Superpixel segmentation via contour optimized non-iterative clustering. In Proceedings of the International Conference on Neural Computing for Advanced Applications. Springer, 645–658.
[30]
Yikai Gong, Richard O. Sinnott, and Paul Rimba. 2018. RT-DBSCAN: Real-time parallel clustering of spatio-temporal data using spark-streaming. In Proceedings of the International Conference on Computational Science (ICCS’18), Yong Shi, Haohuan Fu, Yingjie Tian, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Jack Dongarra, and Peter M. A. Sloot (Eds.). Springer International Publishing, Cham, 524–539.
[31]
Leo Grady. 2006. Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28, 11 (2006), 1768–1783.
[32]
Reaya Grewal, Singara Singh Kasana, and Geeta Kasana. 2023. Hyperspectral image segmentation: A comprehensive survey. Multimedia Tools Appl. 82, 14 (2023), 20819–20872.
[33]
Junyi Guan, Sheng Li, Xiongxiong He, and Jiajia Chen. 2021. Peak-graph-based fast density peak clustering for image segmentation. IEEE Signal Process. Lett. 28 (2021), 897–901.
[34]
Alvaro Guevara, Christian Conrad, and Rudolf Mester. 2011. Boosting segmentation results by contour relaxation. In Proceedings of the 18th IEEE International Conference on Image Processing. IEEE, 1405–1408.
[35]
Laurent Guigues, Jean Pierre Cocquerez, and Hervé Le Men. 2006. Scale-sets image analysis. Int. J. Comput. Vision 68, 3 (2006), 289–317.
[36]
Ashish Kumar Gupta, Ayan Seal, Pritee Khanna, Ondrej Krejcar, and Anis Yazidi. 2021. AWkS: Adaptive, weighted k-means-based superpixels for improved saliency detection. Pattern Anal. Appl. 24, 2 (2021), 625–639.
[37]
Joshua Zhexue Huang, Michael K. Ng, Hongqiang Rong, and Zichen Li. 2005. Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27, 5 (2005), 657–668.
[38]
Varun Jampani, Deqing Sun, Ming-Yu Liu, Ming-Hsuan Yang, and Jan Kautz. 2018. Superpixel sampling networks. In Proceedings of the European Conference on Computer Vision (ECCV’18). 352–368.
[39]
Zexuan Ji, Yubo Huang, Quansen Sun, and Guo Cao. 2016. A spatially constrained generative asymmetric Gaussian mixture model for image segmentation. J. Visual Commun. Image Represent. 40 (2016), 611–626.
[40]
Xuejing Kang, Lei Zhu, and Anlong Ming. 2020. Dynamic random walk for superpixel segmentation. IEEE Trans. Image Process. 29 (2020), 3871–3884.
[41]
Shu Kong and Charless C. Fowlkes. 2018. Recurrent pixel embedding for instance grouping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9018–9028.
[42]
Andreas Krause, Pietro Perona, and Ryan Gomes. 2010. Discriminative clustering by regularized information maximization. Adv. Neural Info. Process. Syst. 23 (2010), 775–783.
[43]
B. Vinoth Kumar. 2023. An extensive survey on superpixel segmentation: A research perspective. Arch. Comput. Methods Eng. (2023), 1–19.
[44]
Victor Lempitsky, Andrea Vedaldi, and Dmitry Ulyanov. 2018. Deep image prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 9446–9454.
[45]
Alex Levinshtein, Adrian Stere, Kiriakos N. Kutulakos, David J. Fleet, Sven J. Dickinson, and Kaleem Siddiqi. 2009. Turbopixels: Fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31, 12 (2009), 2290–2297.
[46]
Hua Li, Yuheng Jia, Runmin Cong, Wenhui Wu, Sam Tak Wu Kwong, and Chuanbo Chen. 2020. Superpixel segmentation based on spatially constrained subspace clustering. IEEE Trans. Industr. Info. 17, 11 (2020), 7501–7512.
[47]
Xiangjun Li, Yong Zhou, Xinping Zhang, Su Xu, and Peng Yu. 2021. Cluster-based fine-to-coarse superpixel segmentation. Eng. Appl. Artific. Intell. 102 (2021), 104281.
[48]
Zhengqin Li and Jiansheng Chen. 2015. Superpixel segmentation using linear spectral clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 1356–1363.
[49]
Yun Liang, Yuqing Zhang, Yihan Wu, Shuqin Tu, and Caixing Liu. 2020. Robust video object segmentation via propagating seams and matching superpixels. IEEE Access 8 (2020), 53766–53776.
[50]
Guohua Liu and Jianchun Duan. 2020. RGB-D image segmentation using superpixel and multi-feature fusion graph theory. Signal, Image Video Process. 14, 6 (2020), 1171–1179.
[51]
Jiafei Liu, Qingsong Wang, Jianda Cheng, Deliang Xiang, and Wenbo Jing. 2022. Multitask learning-based for SAR image superpixel generation. Remote Sens. 14, 4 (2022).
[52]
Ming-Yu Liu, Oncel Tuzel, Srikumar Ramalingam, and Rama Chellappa. 2011. Entropy rate superpixel segmentation. In Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11). 2097–2104.
[53]
Yun Liu, Peng-Tao Jiang, Vahan Petrosyan, Shi-Jie Li, Jiawang Bian, Le Zhang, and Ming-Ming Cheng. 2018. DEL: Deep embedding learning for efficient image segmentation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18). 864–870. https://doi.org/10.24963/ijcai.2018/120
[54]
Seng Cheong Loke, Bruce A. MacDonald, Matthew Parsons, and Burkhard Claus Wünsche. 2021. Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm. J. Real-Time Image Process. 18, 6 (2021), 2361–2376.
[55]
Dongyang Ma, Yuanfeng Zhou, Shiqing Xin, and Wenping Wang. 2020. Convex and compact superpixels by edge-constrained centroidal power diagram. IEEE Trans. Image Process. 30 (2020), 1825–1839.
[56]
Vaïa Machairas, Matthieu Faessel, David Cárdenas-Peña, Théodore Chabardes, Thomas Walter, and Etienne Decenciere. 2015. Waterpixels. IEEE Trans. Image Process. 24, 11 (2015), 3707–3716.
[57]
Georg Maierhofer, Daniel Heydecker, Angelica I. Aviles-Rivero, Samar M. Alsaleh, and Carola-Bibiane Schonlieb. 2018. Peekaboo-where are the objects? Structure adjusting superpixels. In Proceedings of the 25th IEEE International Conference on Image Processing (ICIP’18). 3693–3697.
[58]
Lucy A. C. Mansilla and Paulo A. V. Miranda. 2013. Image segmentation by oriented image foresting transform: Handling ties and colored images. In Proceedings of the 18th International Conference on Digital Signal Processing (DSP’13). IEEE, 1–6.
[59]
Lucy A. C. Mansilla and Paulo A. V. Miranda. 2016. Oriented image foresting transform segmentation: Connectivity constraints with adjustable width. In Proceedings of the 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI’16). 289–296.
[60]
David R. Martin, Charless C. Fowlkes, and Jitendra Malik. 2004. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26, 5 (2004), 530–549.
[61]
Bérengère Mathieu, Alain Crouzil, and Jean Baptiste Puel. 2017. Oversegmentation methods: A new evaluation. In Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis. Springer, 185–193.
[62]
Rudolf Mester, Christian Conrad, and Alvaro Guevara. 2011. Multichannel segmentation using contour relaxation: fast super-pixels and temporal propagation. In Proceedings of the Scandinavian Conference on Image Analysis. Springer, 250–261.
[63]
Alastair P. Moore, Simon J. D. Prince, Jonathan Warrell, Umar Mohammed, and Graham Jones. 2008. Superpixel lattices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1–8.
[64]
Peer Neubert and Peter Protzel. 2012. Superpixel benchmark and comparison. In Proceedings of the Forum Bildverarbeitung, Vol. 6. KIT Scientific Publishing, 1–12.
[65]
Xiao Pan, Yuanfeng Zhou, Yunfeng Zhang, and Caiming Zhang. 2022. Fast generation of superpixels with lattice topology. IEEE Trans. Image Process. 31 (2022), 4828–4841.
[66]
Hankui Peng, Angelica I. Aviles-Rivero, and Carola-Bibiane Schönlieb. 2022. HERS Superpixels: Deep affinity learning for hierarchical entropy rate segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 217–226.
[67]
George Polya. 2020. Mathematics and Plausible Reasoning: Induction and Analogy in Mathematics. Vol. 1. Princeton University Press.
[68]
Nianzu Qiao and Lamei Di. 2022. An improved method of linear spectral clustering. Multimedia Tools Appl. 81, 1 (2022), 1287–1311.
[69]
Lang Ren, Liaoying Zhao, and Yulei Wang. 2019. A superpixel-based dual window RX for hyperspectral anomaly detection. IEEE Geosci. Remote Sens. Lett. 17, 7 (2019), 1233–1237.
[70]
Alex Rodriguez and Alessandro Laio. 2014. Clustering by fast search and find of density peaks. Science 344, 6191 (2014), 1492–1496.
[71]
Buddhadev Sasmal and Krishna Gopal Dhal. 2023. A survey on the utilization of Superpixel image for clustering based image segmentation. Multimedia Tools Appl. 82, 23 (2023), 1–63.
[72]
Alexander Schick, Mika Fischer, and Rainer Stiefelhagen. 2012. Measuring and evaluating the compactness of superpixels. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR’12). IEEE, 930–934.
[73]
Alexander Schick, Mika Fischer, and Rainer Stiefelhagen. 2014. An evaluation of the compactness of superpixels. Pattern Recogn. Lett. 43 (2014), 71–80.
[74]
Philip Sellars, Angelica I. Aviles-Rivero, and Carola-Bibiane Schönlieb. 2020. Superpixel contracted graph-based learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58, 6 (2020), 4180–4193.
[75]
James Albert Sethian. 1999. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Vol. 3. Cambridge University Press.
[76]
Sayed Asad Hussain Shah, Liang Li, Yajun Li, and Jiawan Zhang. 2021. Superpixel segmentation via density peaks. In Proceedings of the 4th International Conference on Information and Computer Technologies (ICICT’21). IEEE, 93–98.
[77]
Jianbing Shen, Xiaopeng Hao, Zhiyuan Liang, Yu Liu, Wenguan Wang, and Ling Shao. 2016. Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Trans. Image Process. 25, 12 (2016), 5933–5942.
[78]
Bin Sheng, Ping Li, Shuangjia Mo, Huating Li, Xuhong Hou, Qiang Wu, Jing Qin, Ruogu Fang, and David Dagan Feng. 2018. Retinal vessel segmentation using minimum spanning superpixel tree detector. IEEE Trans. Cybernet. 49, 7 (2018), 2707–2719.
[79]
Jianping Shi, Qiong Yan, Li Xu, and Jiaya Jia. 2015. Hierarchical image saliency detection on extended CSSD. IEEE Trans. Pattern Anal. Mach. Intell. 38, 4 (2015), 717–729.
[80]
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. 2012. Indoor segmentation and support inference from RGB-D images. In Proceedings of the 12th European Conference on Computer Vision (ECCV’12). Springer, 746–760.
[81]
David Stutz. 2015. Superpixel segmentation: An evaluation. In Proceedings of the German Conference on Pattern Recognition. Springer, 555–562.
[82]
David Stutz, Alexander Hermans, and Bastian Leibe. 2018. Superpixels: An evaluation of the state-of-the-art. Comput. Vision Image Understand. 166 (2018), 1–27.
[83]
Teppei Suzuki. 2020. Superpixel segmentation via convolutional neural networks with regularized information maximization. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’20). 2573–2577.
[84]
Wei-Chih Tu, Ming-Yu Liu, Varun Jampani, Deqing Sun, Shao-Yi Chien, Ming-Hsuan Yang, and Jan Kautz. 2018. Learning superpixels with segmentation-aware affinity loss. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 568–576.
[85]
Shakir Ullah, Naeem Bhatti, and Muhammad Zia. 2021. Adaptive tuning of SLIC parameter K. Multimedia Tools Appl. 80, 17 (2021), 25649–25672.
[86]
Michael Van den Bergh, Xavier Boix, Gemma Roig, Benjamin de Capitani, and Luc Van Gool. 2012. SEEDS: Superpixels extracted via energy-driven sampling. In Proceedings of the European Conference on Computer Vision (ECCV’12), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, and Cordelia Schmid (Eds.). Springer, Berlin, 13–26.
[87]
Michael Van den Bergh, Xavier Boix, Gemma Roig, and Luc Van Gool. 2015. Seeds: Superpixels extracted via energy-driven sampling. Int. J. Comput. Vision 111, 3 (2015), 298–314.
[88]
John E. Vargas-Muñoz, Ananda S. Chowdhury, Eduardo B. Alexandre, Felipe L. Galvão, Paulo A. Vechiatto Miranda, and Alexandre X. Falcão. 2019. An iterative spanning forest framework for superpixel segmentation. IEEE Trans. Image Process. 28, 7 (2019), 3477–3489.
[89]
Gaochao Wang, Yiheng Wei, and Peter Tse. 2018. Clustering by defining and merging candidates of cluster centers via independence and affinity. Neurocomputing 315 (2018), 486–495.
[90]
Hui Wang, Jianbing Shen, Junbo Yin, Xingping Dong, Hanqiu Sun, and Ling Shao. 2020. Adaptive nonlocal random walks for image superpixel segmentation. IEEE Trans. Circ. Syst. Video Technol. 30, 3 (2020), 822–834.
[91]
Jingjing Wang, Zhenye Luan, Zishu Yu, Jinwen Ren, Jun Gao, Kejiang Yuan, and Huaqiang Xu. 2022. Superpixel segmentation with squeeze-and-excitation networks. Signal, Image Video Process. 16, 5 (2022), 1–8.
[92]
Kai Wang, Liang Li, and Jiawan Zhang. 2020. End-to-end trainable network for superpixel and image segmentation. Pattern Recogn. Lett. 140 (2020), 135–142.
[93]
Murong Wang, Xiabi Liu, Yixuan Gao, Xiao Ma, and Nouman Q. Soomro. 2017. Superpixel segmentation: A benchmark. Signal Process.: Image Commun. 56 (2017), 28–39.
[94]
Nannan Wang and Yongxia Zhang. 2021. Adaptive and fast image superpixel segmentation approach. Image Vision Comput. 116 (2021), 104315.
[95]
Weiwei Wang and Cuiling Wu. 2017. Image segmentation by correlation adaptive weighted regression. Neurocomputing 267 (2017), 426–435.
[96]
Xuehui Wang, Qingyun Zhao, Lei Fan, Yuzhi Zhao, Tiantian Wang, Qiong Yan, and Long Chen. 2021. Semasuperpixel: A multi-channel probability-driven superpixel segmentation method. In Proceedings of the IEEE International Conference on Image Processing (ICIP’21). IEEE, 1859–1863.
[97]
Yaxiong Wang, Yunchao Wei, Xueming Qian, Li Zhu, and Yi Yang. 2021. AINet: Association implantation for superpixel segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7078–7087.
[98]
Xing Wei, Qingxiong Yang, Yihong Gong, Narendra Ahuja, and Ming-Hsuan Yang. 2018. Superpixel hierarchy. IEEE Trans. Image Process. 27, 10 (2018), 4838–4849.
[99]
Douglas Brent West et al. 2001. Introduction to Graph Theory. Vol. 2. Prentice Hall Upper Saddle River.
[100]
Jiang Wu, Chunxiao Liu, and Biao Li. 2021. Texture-aware and structure-preserving superpixel segmentation. Comput. Graph. 94 (2021), 152–163.
[101]
Ruiqi Wu, Yajuan Du, Hua Li, and Yucong Dai. 2021. Stereo superpixel segmentation via dual-attention fusion networks. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME’21). IEEE, 1–6.
[102]
Xiang Wu, Yufei Chen, Xianhui Liu, Jianan Shen, Keqiang Zhuo, and Weidong Zhao. 2020. Superpixel via coarse-to-fine boundary shift. Appl. Intell. 50, 7 (2020), 2079–2092.
[103]
Fengting Yang, Qian Sun, Hailin Jin, and Zihan Zhou. 2020. Superpixel segmentation with fully convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20).
[104]
Jian Yao, Marko Boben, Sanja Fidler, and Raquel Urtasun. 2015. Real-time coarse-to-fine topologically preserving segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2947–2955.
[105]
Yue Yu, Yang Yang, and Kezhao Liu. 2021. Edge-aware superpixel segmentation with unsupervised convolutional neural networks. In Proceedings of the IEEE International Conference on Image Processing (ICIP’21). IEEE, 1504–1508.
[106]
Hongxing Yuan, Shaoqun Wu, Peihong Cheng, Peng An, and Shudi Bao. 2015. Nonlocal random walks algorithm for semi-automatic 2D-to-3D image conversion. IEEE Signal Process. Lett. 22, 3 (2015), 371–374.
[107]
Qing Yuan, Songfeng Lu, Yan Huang, and Wuxin Sha. 2021. SIN: Superpixel interpolation network. In Proceedings of the Pacific Rim International Conference on Artificial Intelligence. Springer, 293–307.
[108]
Ye Yuan, Wei Zhang, Hai Yu, and Zhiliang Zhu. 2021. Superpixels with content-adaptive criteria. IEEE Trans. Image Process. 30 (2021), 7702–7716.
[109]
Ye Yuan, Zhiliang Zhu, Hai Yu, and Wei Zhang. 2020. Watershed-based superpixels with global and local boundary marching. IEEE Trans. Image Process. 29 (2020), 7375–7388.
[110]
Bin Zhang, Xuejing Kang, and Anlong Ming. 2021. BP-net: Deep learning-based superpixel segmentation for RGB-D image. In Proceedings of the 25th International Conference on Pattern Recognition (ICPR’20). IEEE, 7433–7438.
[111]
Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, and Carola-Bibiane Schönlieb. 2021. Dynamic spectral residual superpixels. Pattern Recogn. 112 (2021), 107705.
[112]
Jianhua Zhang, Jingbo Chen, Qichao Wang, and Shengyong Chen. 2019. Spatiotemporal saliency detection based on maximum consistency superpixels merging for video analysis. IEEE Trans. Industr. Inform. 16, 1 (2019), 606–614.
[113]
Yongxia Zhang, Qiang Guo, and Caiming Zhang. 2021. Simple and fast image superpixels generation with color and boundary probability. Visual Comput. 37 (2021), 1061–1074.
[114]
Wei Zhao, Yi Fu, Xiaosong Wei, and Hai Wang. 2018. An improved image semantic segmentation method based on superpixels and conditional random fields. Appl. Sci. 8, 5 (2018), 837.
[115]
Xianen Zhou, Yaonan Wang, Qing Zhu, Changyan Xiao, and Xiao Lu. 2019. SSG: Superpixel segmentation and grabcut-based salient object segmentation. Visual Comput. 35, 3 (2019), 385–398.
[116]
Lei Zhu, Qi She, Bin Zhang, Yanye Lu, Zhilin Lu, Duo Li, and Jie Hu. 2021. Learning the superpixel in a non-iterative and lifelong manner. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1225–1234.
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- A Comprehensive Review and New Taxonomy on Superpixel Segmentation
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Published In
August 2024
963 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3613627
- Editors:
- David Atienza,
- Michela Milano
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 April 2024
Online AM: 12 March 2024
Accepted: 07 March 2024
Revised: 20 February 2024
Received: 09 February 2023
Published in CSUR Volume 56, Issue 8
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- Survey
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- Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq
- Fundação de Amparo a Pesquisa do Estado de Minas Gerais—FAPEMIG
- Fundação de Amparo a Pesquisa do Estado de São Paulo—FAPESP
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
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