{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:13:58Z","timestamp":1760238838002,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571346","51805398"],"award-info":[{"award-number":["61571346","51805398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper exploits a concise yet efficient initialization strategy to optimize grid sampling-based superpixel segmentation algorithms. Rather than straight distributing all initial seeds evenly, it adopts a context-aware approach to modify their positions and total number via a coarse-to-fine manner. Firstly, half the expected number of seeds are regularly sampled on the image grid, thereby creating a rough distribution of color information for all rectangular cells. A series of fission is then performed on cells that contain excessive color information recursively. In each cell, the local color uniformity is balanced by a dichotomy on one original seed, which generates two new seeds and settles them to spatially symmetrical sub-regions. Therefore, the local concentration of seeds is adaptive to the complexity of regional information. In addition, by calculating the amount of color via a summed area table (SAT), the informative regions can be located at a very low time cost. As a result, superpixels are produced from ideal original seeds with an exact number and exhibit better boundary adherence. Experiments demonstrate that the proposed strategy effectively promotes the performance of simple linear iterative clustering (SLIC) and its variants in terms of several quality measures.<\/jats:p>","DOI":"10.3390\/sym12091417","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T09:05:37Z","timestamp":1598432737000},"page":"1417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GRID: GRID Resample by Information Distribution"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3915-5451","authenticated-orcid":false,"given":"Cheng","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Intelligent Control and Image Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baolong","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Control and Image Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Control and Image Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianglei","family":"Gong","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Control and Image Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"China Academy of Space Technology, Haidian 100094, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Han","sequence":"additional","affiliation":[{"name":"China Academy of Space Technology, Haidian 100094, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wangpeng","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Control and Image Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ren, X., and Malik, J. (2003, January 13\u201316). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Nice, France.","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, K., and Sim, J. (2020, July 25). Warping Residual Based Image Stitching for Large Parallax. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Available online: http:\/\/cvpr20.com\/.","DOI":"10.1109\/CVPR42600.2020.00822"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yeo, D., Son, J., Han, B., and Han, J. (2017, January 21\u201326). Superpixel-based tracking-by-segmentation using Markov chains. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.62"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/TPAMI.2016.2610973","article-title":"Gamifying video object segmentation","volume":"39","author":"Spampinato","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/TIP.2016.2524198","article-title":"Dense and sparse reconstruction error based saliency descriptor","volume":"25","author":"Lu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4889","DOI":"10.1109\/TIP.2018.2839524","article-title":"Accurate light field depth estimation with superpixel regularization over partially occluded regions","volume":"27","author":"Chen","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","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":"Comput. Vis. Image Underst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1109\/TPAMI.2006.233","article-title":"Random walks for image segmentation","volume":"28","author":"Grady","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, M., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"ref_11","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":"ref_12","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."},{"key":"ref_13","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":"ref_14","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/34.87344","article-title":"Watersheds in digital spaces: An efficient algorithm based on immersion simulations","volume":"13","author":"Vincent","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","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 Underst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TPAMI.2017.2686857","article-title":"Intrinsic manifold SLIC: A simple and efficient method for computing content-sensitive superpixels","volume":"40","author":"Liu","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, J., Hou, Q., Ren, B., Cheng, M., and Rosin, P. (2018, January 2\u20137). FLIC: Fast linear iterative clustering with active search. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12286"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2017, January 21\u201326). Superpixels and polygons using simple non-iterative clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.1109\/TIP.2018.2848548","article-title":"USEAQ: Ultra-fast superpixel extraction via adaptive sampling from quantized regions","volume":"27","author":"Huang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, Z., Zou, Q., and Li, Q. (2015, January 27\u201330). Watershed superpixel. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350818"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Neubert, P., and Protzel, P. (2014, January 24\u201328). Compact Watershed and Preemptive SLIC: On improving trade-offs of superpixel segmentation algorithms. Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.181"},{"key":"ref_22","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":"ref_23","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":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, L., Luo, B., Pei, Z., and Qin, K. (2018). PFS: Particle-filter-based superpixel segmentation. Symmetry, 10.","DOI":"10.3390\/sym10050143"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1137\/S0036144599352836","article-title":"Centroidal Voronoi tessellations: Applications and algorithms","volume":"41","author":"Du","year":"1999","journal-title":"SIAM Rev."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, B., Wang, G., Zheng, Y., Liu, Y., and He, W. (2020). NICE: Superpixel segmentation using non-iterative clustering with efficiency. Appl. Sci., 10.","DOI":"10.3390\/app10124415"},{"key":"ref_28","unstructured":"Meyer, F. (1992, January 7\u201311). Color image segmentation. Proceedings of the International Conference on Image Processing (ICIP), Singapore."},{"key":"ref_29","unstructured":"Achanta, R., Marquez, P., Fua, P., and Susstrunk, S. (2018, January 12\u201316). Scale-adaptive superpixels. Proceedings of the IS&T Color and Imaging Conference (CIC), Vancouver, BC, Canada."},{"key":"ref_30","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA."},{"key":"ref_31","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."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.image.2017.04.007","article-title":"Superpixel segmentation: A benchmark","volume":"56","author":"Wang","year":"2017","journal-title":"Signal Process. Image Commun."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/9\/1417\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:51Z","timestamp":1760177211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/9\/1417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,26]]},"references-count":32,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["sym12091417"],"URL":"https:\/\/doi.org\/10.3390\/sym12091417","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,8,26]]}}}