{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T18:56:21Z","timestamp":1768071381158,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,17]],"date-time":"2018-05-17T00:00:00Z","timestamp":1526515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China for Young Scientists","award":["61502065"],"award-info":[{"award-number":["61502065"]}]},{"name":"the Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission","award":["cstc2015jcyjBX0127,  cstc2017jcyjBX0059"],"award-info":[{"award-number":["cstc2015jcyjBX0127,  cstc2017jcyjBX0059"]}]},{"name":"the Humanities and Social Sciences Research Key Program of Chongqing Municipal Education Commission","award":["17SKG136"],"award-info":[{"award-number":["17SKG136"]}]},{"name":"the Scientific and Technological Research Program of Chongqing Municipal Education Commission","award":["KJ1500922,  KJ1600945"],"award-info":[{"award-number":["KJ1500922,  KJ1600945"]}]},{"name":"the Foundation and Frontier Research Program of Chongqing Science and Technology Commission","award":["cstc2017jcyjAX0144"],"award-info":[{"award-number":["cstc2017jcyjAX0144"]}]},{"name":"the Youth Spark Support Project of Chongqing University of Technology","award":["2015XH16"],"award-info":[{"award-number":["2015XH16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Image segmentation is a challenging task in the field of image processing and computer vision. In order to obtain an accurate segmentation performance, user interaction is always used in practical image-segmentation applications. However, a good segmentation method should not rely on much prior information. In this paper, an efficient superpixel-guided interactive image-segmentation algorithm based on graph theory is proposed. In this algorithm, we first perform the initial segmentation by using the MeanShift algorithm, then a graph is built by taking the pre-segmented regions (superpixels) as nodes, and the maximum flow\u2013minimum cut algorithm is applied to get the superpixel-level segmentation solution. In this process, each superpixel is represented by a color histogram, and the Bhattacharyya coefficient is chosen to calculate the similarity between any two adjacent superpixels. Considering the over-segmentation problem of the MeanShift algorithm, a narrow band is constructed along the contour of objects using a morphology operator. In order to further segment the pixels around edges accurately, a graph is created again for those pixels in the narrow band and, following the maximum flow\u2013minimum cut algorithm, the final pixel-level segmentation is completed. Extensive experimental results show that the presented algorithm obtains much more accurate segmentation results with less user interaction and less running time than the widely used GraphCut algorithm, Lazy Snapping algorithm, GrabCut algorithm and a region merging algorithm based on maximum similarity (MSRM).<\/jats:p>","DOI":"10.3390\/sym10050169","type":"journal-article","created":{"date-parts":[[2018,5,17]],"date-time":"2018-05-17T11:47:45Z","timestamp":1526557665000},"page":"169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Efficient Superpixel-Guided Interactive Image Segmentation Based on Graph Theory"],"prefix":"10.3390","volume":"10","author":[{"given":"Jianwu","family":"Long","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanglei","family":"Gou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yilmaz, A., Javed, O., and Shah, M. (2006). Object tracking: A survey. ACM Comput. Surv., 38.","DOI":"10.1145\/1177352.1177355"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.trit.2016.03.005","article-title":"Background modeling methods in video analysis: A review and comparative evaluation","volume":"1","author":"Xu","year":"2016","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.cviu.2013.04.005","article-title":"50 years of object recognition: Directions forward","volume":"117","author":"Andreopoulos","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Popescu, D., and Ichim, L. (2018). Intelligent Image Processing System for Detection and Segmentation of Regions of Interest in Retinal Images. Symmetry, 10.","DOI":"10.3390\/sym10030073"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1117\/1.1631315","article-title":"Survey over image thresholding techniques and quantitative performance evaluation","volume":"13","author":"Sezgin","year":"2004","journal-title":"J. Electron. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vantaram, S.R., and Saber, E. (2012). Survey of contemporary trends in color image segmentation. J. Electron. Imaging, 21.","DOI":"10.1117\/1.JEI.21.4.040901"},{"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":"1134","DOI":"10.3724\/SP.J.1004.2012.01134","article-title":"Adaptive minimum error thresholding algorithm","volume":"38","author":"Long","year":"2012","journal-title":"Zidonghua Xuebao\/Acta Autom. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1049\/trit.2017.0013","article-title":"Retinal Image Segmentation Using Double-Scale Nonlinear Thresholding on Vessel Support Regions","volume":"2","author":"Li","year":"2017","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_10","first-page":"1108","article-title":"Otsu thresholding algorithm based on rebuilding and dimension reduction of the 3-dimensional histogram","volume":"39","author":"Shen","year":"2011","journal-title":"Tien Tzu Hsueh Pao\/Acta Electron. Sin."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Guo, Y., Akbulut, Y., \u015eeng\u00fcr, A., Xia, R., and Smarandache, F. (2017). An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut. Symmetry, 9.","DOI":"10.3390\/sym9090185"},{"key":"ref_12","first-page":"1420","article-title":"Interactive document images thresholding segmentation algorithm based on image regions","volume":"49","author":"Long","year":"2012","journal-title":"Jisuanji Yanjiu Yu Fazhan\/Comput. Res. Dev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TPAMI.2004.60","article-title":"An experimental comparison of min-cut\/max-flow algorithms for energy minimization in vision","volume":"26","author":"Boykov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","article-title":"Graph cuts and efficient nd image segmentation","volume":"70","author":"Boykov","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, D., Li, G., Sun, Y., Kong, J., Jiang, G., Tang, H., Ju, Z., Yu, H., and Liu, H. (2017). An interactive image segmentation method in hand gesture recognition. Sensors, 17.","DOI":"10.3390\/s17020253"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.patcog.2009.03.008","article-title":"A comparative evaluation of interactive segmentation algorithms","volume":"43","author":"McGuinness","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1145\/1015706.1015719","article-title":"Lazy snapping","volume":"23","author":"Li","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"Grabcut: Interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.patcog.2009.03.004","article-title":"Interactive image segmentation by maximal similarity based region merging","volume":"43","author":"Ning","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vincent, L., and Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell., 583\u2013598.","DOI":"10.1109\/34.87344"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.jvcir.2015.09.015","article-title":"Automated coronal hole segmentation from solar euv images using the watershed transform","volume":"33","author":"Ciecholewski","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_23","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."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2009.71","article-title":"Watershed cuts: Thinnings, shortest path forests, and topological watersheds","volume":"32","author":"Cousty","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","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":"ref_27","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1109\/78.107417","article-title":"Color quantization of images","volume":"39","author":"Orchard","year":"1991","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/TPAMI.2007.1046","article-title":"Toward objective evaluation of image segmentation algorithms","volume":"29","author":"Unnikrishnan","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. 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