{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T14:11:28Z","timestamp":1778767888920,"version":"3.51.4"},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["9610034"],"award-info":[{"award-number":["9610034"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["9610460"],"award-info":[{"award-number":["9610460"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003452","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003452","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"University Grants Committee Research Grants Council","doi-asserted-by":"publisher","award":["11204821"],"award-info":[{"award-number":["11204821"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Signal Processing: Image Communication"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.image.2026.117563","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T23:13:28Z","timestamp":1776294808000},"page":"117563","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A survey of superpixel methods and their applications"],"prefix":"10.1016","volume":"145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3405-742X","authenticated-orcid":false,"given":"Chong","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.image.2026.117563_b1","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117563_b2","series-title":"2003 IEEE International Conference on Computer Vision","first-page":"10","article-title":"Learning a classification model for segmentation","volume":"vol. 1","author":"Ren","year":"2003"},{"key":"10.1016\/j.image.2026.117563_b3","series-title":"2014 IEEE International Conference on Image Processing","first-page":"947","article-title":"Fast and robust image segmentation using an superpixel based FCM algorithm","author":"Jia","year":"2014"},{"issue":"1","key":"10.1016\/j.image.2026.117563_b4","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11263-011-0449-8","article-title":"Harmony potentials","volume":"96","author":"Boix","year":"2012","journal-title":"Int. J. Comput. Vis."},{"issue":"5","key":"10.1016\/j.image.2026.117563_b5","doi-asserted-by":"crossref","first-page":"2846","DOI":"10.1109\/TFUZZ.2018.2814591","article-title":"Fuzzy superpixels for polarimetric SAR images classification","volume":"26","author":"Guo","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.image.2026.117563_b6","series-title":"European Conference on Computer Vision","first-page":"400","article-title":"Structured image segmentation using kernelized features","author":"Lucchi","year":"2012"},{"key":"10.1016\/j.image.2026.117563_b7","series-title":"Segment anything","author":"Kirillov","year":"2023"},{"issue":"6","key":"10.1016\/j.image.2026.117563_b8","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TCSVT.2020.3019109","article-title":"Fuzzy SLIC: Fuzzy simple linear iterative clustering","volume":"31","author":"Wu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.image.2026.117563_b9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Computer Vis. Image Understanding"},{"key":"10.1016\/j.image.2026.117563_b10","series-title":"2011 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2097","article-title":"Entropy rate superpixel segmentation","author":"Liu","year":"2011"},{"issue":"7","key":"10.1016\/j.image.2026.117563_b11","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.1109\/TIP.2019.2897941","article-title":"An iterative spanning forest framework for superpixel segmentation","volume":"28","author":"Vargas-Mu\u00f1oz","year":"2019","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.image.2026.117563_b12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TPAMI.2004.1261076","article-title":"The image foresting transform: theory, algorithms, and applications","volume":"26","author":"Falcao","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117563_b13","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/LSP.2020.3015433","article-title":"Superpixel segmentation using dynamic and iterative spanning forest","volume":"27","author":"Bel\u00e9m","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"issue":"4","key":"10.1016\/j.image.2026.117563_b14","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TIP.2014.2302892","article-title":"Lazy random walks for superpixel segmentation","volume":"23","author":"Shen","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b15","doi-asserted-by":"crossref","first-page":"3871","DOI":"10.1109\/TIP.2020.2967583","article-title":"Dynamic random walk for superpixel segmentation","volume":"29","author":"Kang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10.1016\/j.image.2026.117563_b16","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2008.173","article-title":"Watershed cuts: Minimum spanning forests and the drop of water principle","volume":"31","author":"Cousty","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"10.1016\/j.image.2026.117563_b17","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TIP.2015.2451011","article-title":"Waterpixels","volume":"24","author":"Machairas","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b18","series-title":"International Conference on Pattern Recognition","first-page":"996","article-title":"Compact watershed and preemptive SLIC: On improving trade-offs of superpixel segmentation algorithms","author":"Neubert","year":"2014"},{"key":"10.1016\/j.image.2026.117563_b19","unstructured":"W. Benesova, M. Kottman, Fast superpixel segmentation using morphological processing, in: Conference on Machine Vision and Machine Learning, 2014, 67\u20131\u201367\u20139."},{"key":"10.1016\/j.image.2026.117563_b20","series-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1356","article-title":"Superpixel segmentation using linear spectral clustering","author":"Li","year":"2015"},{"key":"10.1016\/j.image.2026.117563_b21","series-title":"European Conference on Computer Vision","first-page":"352","article-title":"Superpixel sampling networks","author":"Jampani","year":"2018"},{"key":"10.1016\/j.image.2026.117563_b22","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s41095-018-0123-y","article-title":"FLIC: Fast linear iterative clustering with active search","volume":"4","author":"Zhao","year":"2018","journal-title":"Comput. Vis. Media"},{"key":"10.1016\/j.image.2026.117563_b23","series-title":"2017 IEEE Computer Vision and Pattern Recognition","first-page":"4895","article-title":"Superpixels and polygons using simple non-iterative clustering","author":"Achanta","year":"2017"},{"key":"10.1016\/j.image.2026.117563_b24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2018.01.006","article-title":"Robust superpixels using color and contour features along linear path","volume":"170","author":"Giraud","year":"2018","journal-title":"Comput. Vis. Image Understanding"},{"key":"10.1016\/j.image.2026.117563_b25","doi-asserted-by":"crossref","first-page":"77250","DOI":"10.1109\/ACCESS.2021.3081919","article-title":"Iterative boundaries implicit identification for superpixels segmentation: A real-time approach","volume":"9","author":"Bobbia","year":"2021","journal-title":"IEEE Access"},{"issue":"8","key":"10.1016\/j.image.2026.117563_b26","doi-asserted-by":"crossref","first-page":"4105","DOI":"10.1109\/TIP.2018.2836306","article-title":"Superpixel segmentation using Gaussian mixture model","volume":"27","author":"Ban","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.imavis.2017.12.001","article-title":"Minimum barrier superpixel segmentation","volume":"70","author":"Hu","year":"2018","journal-title":"Image Vis. Comput."},{"issue":"2","key":"10.1016\/j.image.2026.117563_b28","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1109\/TBDATA.2024.3423719","article-title":"Adaptive superpixel segmentation with non-uniform seed initialization","volume":"11","author":"Xie","year":"2025","journal-title":"IEEE Trans. Big Data"},{"issue":"12","key":"10.1016\/j.image.2026.117563_b29","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"TurboPixels: Fast superpixels using geometric flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117563_b30","series-title":"Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th International Conference, EMMCVPR 2013, Lund, Sweden, August 19-21, 2013. Proceedings 9","first-page":"280","article-title":"Contour-relaxed superpixels","author":"Conrad","year":"2013"},{"key":"10.1016\/j.image.2026.117563_b31","series-title":"European Conference on Computer Vision","first-page":"13","article-title":"SEEDS: Superpixels extracted via energy-driven sampling","author":"Bergh","year":"2012"},{"key":"10.1016\/j.image.2026.117563_b32","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1109\/TIP.2023.3234700","article-title":"Vine spread for superpixel segmentation","volume":"32","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"10.1016\/j.image.2026.117563_b33","doi-asserted-by":"crossref","first-page":"4838","DOI":"10.1109\/TIP.2018.2836300","article-title":"Superpixel hierarchy","volume":"27","author":"Wei","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b34","article-title":"GMMSP on GPU","author":"Ban","year":"2018","journal-title":"J. Real-Time Image Processing"},{"issue":"6","key":"10.1016\/j.image.2026.117563_b35","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1109\/TIP.2018.2810541","article-title":"Content-adaptive superpixel segmentation","volume":"27","author":"Xiao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b36","series-title":"2019 IEEE\/CVF International Conference on Computer Vision","first-page":"3769","article-title":"Fast computation of content-sensitive superpixels and supervoxels using Q-distances","author":"Ye","year":"2019"},{"key":"10.1016\/j.image.2026.117563_b37","series-title":"2019 IEEE\/CVF International Conference on Computer Vision","first-page":"8469","article-title":"Bayesian adaptive superpixel segmentation","author":"Uziel","year":"2019"},{"key":"10.1016\/j.image.2026.117563_b38","doi-asserted-by":"crossref","first-page":"7375","DOI":"10.1109\/TIP.2020.3002078","article-title":"Watershed-based superpixels with global and local boundary marching","volume":"29","author":"Yuan","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b39","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13961","article-title":"Superpixel segmentation with fully convolutional networks","author":"Yang","year":"2020"},{"key":"10.1016\/j.image.2026.117563_b40","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1109\/TIP.2020.3045640","article-title":"Convex and compact superpixels by edge-constrained centroidal power diagram","volume":"30","author":"Ma","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b41","doi-asserted-by":"crossref","unstructured":"L. Zhu, Q. She, B. Zhang, Y. Lu, Z. Lu, D. Li, J. Hu, Learning the superpixel in a non-iterative and lifelong manner, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 1225\u20131234.","DOI":"10.1109\/CVPR46437.2021.00128"},{"key":"10.1016\/j.image.2026.117563_b42","doi-asserted-by":"crossref","unstructured":"Y. Wang, Y. Wei, X. Qian, L. Zhu, Y. Yang, AINet: Association implantation for superpixel segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 7078\u20137087.","DOI":"10.1109\/ICCV48922.2021.00699"},{"key":"10.1016\/j.image.2026.117563_b43","doi-asserted-by":"crossref","first-page":"7702","DOI":"10.1109\/TIP.2021.3108403","article-title":"Superpixels with content-adaptive criteria","volume":"30","author":"Yuan","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b44","article-title":"High quality superpixel generation through regional decomposition","author":"Xu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.image.2026.117563_b45","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"568","article-title":"Learning superpixels with segmentation-aware affinity loss","author":"Tu","year":"2018"},{"key":"10.1016\/j.image.2026.117563_b46","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"651","article-title":"Manifold SLIC: A fast method to compute content-sensitive superpixels","author":"Liu","year":"2016"},{"issue":"11","key":"10.1016\/j.image.2026.117563_b47","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TCSVT.2016.2589781","article-title":"Superpixels by bilateral geodesic distance","volume":"27","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.image.2026.117563_b48","series-title":"2016 23rd International Conference on Pattern Recognition","first-page":"2824","article-title":"BASS: Boundary-aware superpixel segmentation","author":"Rubio","year":"2016"},{"key":"10.1016\/j.image.2026.117563_b49","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"5382","article-title":"Bayesian-inspired space-time superpixels","author":"Gauen","year":"2025"},{"key":"10.1016\/j.image.2026.117563_b50","doi-asserted-by":"crossref","first-page":"4719","DOI":"10.1109\/TIP.2022.3187563","article-title":"Hierarchical superpixel segmentation by parallel CRTrees labeling","volume":"31","author":"Yan","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b51","doi-asserted-by":"crossref","unstructured":"H. Peng, A.I. Aviles-Rivero, C.-B. Sch\u00f6nlieb, HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 217\u2013226.","DOI":"10.1109\/WACV51458.2022.00015"},{"key":"10.1016\/j.image.2026.117563_b52","series-title":"36th British Machine Vision Conference","article-title":"Superpixel anything: A general object-based framework for accurate yet regular superpixel segmentation","author":"Walther","year":"2025"},{"key":"10.1016\/j.image.2026.117563_b53","doi-asserted-by":"crossref","unstructured":"A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.-Y. Lo, P. Dollar, R. Girshick, Segment Anything, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2023, pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"5","key":"10.1016\/j.image.2026.117563_b54","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","article-title":"Contour detection and hierarchical image segmentation","volume":"33","author":"Arbelaez","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.image.2026.117563_b55","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"issue":"6","key":"10.1016\/j.image.2026.117563_b56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JEI.26.6.061603","article-title":"Evaluation framework of superpixel methods with a global regularity measure","volume":"26","author":"Giraud","year":"2017","journal-title":"J. Electron. Imaging"},{"issue":"7","key":"10.1016\/j.image.2026.117563_b57","first-page":"1502","article-title":"A simple algorithm of superpixel segmentation with boundary constraint","volume":"27","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.image.2026.117563_b58","series-title":"2019 IEEE International Conference on Image Processing","first-page":"1455","article-title":"Improved superpixel-based fast fuzzy C-means clustering for image segmentation","author":"Wu","year":"2019"},{"key":"10.1016\/j.image.2026.117563_b59","doi-asserted-by":"crossref","first-page":"147462","DOI":"10.1109\/ACCESS.2019.2946939","article-title":"Superpixel tensor pooling for visual tracking using multiple midlevel visual cues fusion","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.image.2026.117563_b60","series-title":"2013 IEEE International Conference on Computer Vision","first-page":"377","article-title":"Online video SEEDS for temporal window objectness","author":"Bergh","year":"2013"},{"issue":"10","key":"10.1016\/j.image.2026.117563_b61","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.1587\/transinf.2020EDL8025","article-title":"Superpixel based hierarchical segmentation for color image","volume":"E103.D","author":"Wu","year":"2020","journal-title":"IEICE Trans. Inf. Syst."},{"issue":"9","key":"10.1016\/j.image.2026.117563_b62","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1109\/TFUZZ.2018.2889018","article-title":"Superpixel-based fast fuzzy C-means clustering for color image segmentation","volume":"27","author":"Lei","year":"2019","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"2","key":"10.1016\/j.image.2026.117563_b63","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1007\/s10278-018-0149-9","article-title":"Suspicious lesion segmentation on brain, mammograms and breast MR images using new optimized spatial feature based super-pixel fuzzy c-means clustering","volume":"32","author":"Kumar","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.image.2026.117563_b64","series-title":"2022 International Joint Conference on Neural Networks","first-page":"01","article-title":"Graph neural network and superpixel based brain tissue segmentation","author":"Wu","year":"2022"},{"key":"10.1016\/j.image.2026.117563_b65","series-title":"2011 IEEE International Conference on Computer Vision","first-page":"1323","article-title":"Superpixel tracking","author":"Wang","year":"2011"},{"issue":"4","key":"10.1016\/j.image.2026.117563_b66","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1109\/TIP.2014.2300823","article-title":"Robust superpixel tracking","volume":"23","author":"Yang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.image.2026.117563_b67","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"key":"10.1016\/j.image.2026.117563_b68","series-title":"2007 IEEE International Conference on Computer Vision","first-page":"1","article-title":"Robust visual tracking based on incremental tensor subspace learning","author":"Li","year":"2007"},{"key":"10.1016\/j.image.2026.117563_b69","series-title":"European Conference on Computer Vision","first-page":"211","article-title":"Superpixels and supervoxels in an energy optimization framework","author":"Veksler","year":"2010"},{"key":"10.1016\/j.image.2026.117563_b70","series-title":"2013 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2027","article-title":"Voxel cloud connectivity segmentation - supervoxels for point clouds","author":"Papon","year":"2013"},{"key":"10.1016\/j.image.2026.117563_b71","series-title":"2013 IEEE International Conference on Computer Vision","first-page":"385","article-title":"Temporally consistent superpixels","author":"Reso","year":"2013"},{"key":"10.1016\/j.image.2026.117563_b72","doi-asserted-by":"crossref","first-page":"9665","DOI":"10.1109\/TIP.2020.3030502","article-title":"Real-time hierarchical supervoxel segmentation via a minimum spanning tree","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117563_b73","series-title":"The 39th Annual Conference on Neural Information Processing Systems","article-title":"DuSA: Fast and accurate dual-stage sparse attention mechanism accelerating both training and inference","author":"Wu","year":"2025"},{"key":"10.1016\/j.image.2026.117563_b74","series-title":"Proceedings of the 33rd ACM International Conference on Multimedia","first-page":"9140","article-title":"ELFATT: Efficient linear fast attention for vision transformers","author":"Wu","year":"2025"},{"issue":"4","key":"10.1016\/j.image.2026.117563_b75","doi-asserted-by":"crossref","first-page":"4792","DOI":"10.1109\/TPAMI.2025.3646452","article-title":"The CUR decomposition of self-attention matrices in vision transformers","volume":"48","author":"Wu","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Signal Processing: Image Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092359652600086X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092359652600086X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T13:27:07Z","timestamp":1778765227000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092359652600086X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":75,"alternative-id":["S092359652600086X"],"URL":"https:\/\/doi.org\/10.1016\/j.image.2026.117563","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.23682945.v2","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.23682945.v1","asserted-by":"object"}]},"ISSN":["0923-5965"],"issn-type":[{"value":"0923-5965","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A survey of superpixel methods and their applications","name":"articletitle","label":"Article Title"},{"value":"Signal Processing: Image Communication","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.image.2026.117563","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"117563"}}