{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:15:36Z","timestamp":1760242536884,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,10,2]],"date-time":"2017-10-02T00:00:00Z","timestamp":1506902400000},"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":["61603258","61703280","61772018"],"award-info":[{"award-number":["61603258","61703280","61772018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the public welfare technology application research project of Zhejiang province","award":["2016C31082"],"award-info":[{"award-number":["2016C31082"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Visual object tracking is a critical task in computer vision. Challenging things always exist when an object needs to be tracked. For instance, background clutter is one of the most challenging problems. The mean-shift tracker is quite popular because of its efficiency and performance in a range of conditions. However, the challenge of background clutter also disturbs its performance. In this article, we propose a novel weighted histogram based on neutrosophic similarity score to help the mean-shift tracker discriminate the target from the background. Neutrosophic set (NS) is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS to improve the mean-shift tracker. First, two kinds of criteria are considered as the object feature similarity and the background feature similarity, and each bin of the weight histogram is represented in the SVNS domain via three membership functions T(Truth), I(indeterminacy), and F(Falsity). Second, the neutrosophic similarity score function is introduced to fuse those two criteria and to build the final weight histogram. Finally, a novel neutrosophic weighted mean-shift tracker is proposed. The proposed tracker is compared with several mean-shift based trackers on a dataset of 61 public sequences. The results revealed that our method outperforms other trackers, especially when confronting background clutter.<\/jats:p>","DOI":"10.3390\/info8040122","type":"journal-article","created":{"date-parts":[[2017,10,2]],"date-time":"2017-10-02T13:10:05Z","timestamp":1506949805000},"page":"122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5628-7640","authenticated-orcid":false,"given":"Keli","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1354-1354","authenticated-orcid":false,"given":"En","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2841-6529","authenticated-orcid":false,"given":"Jun","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering, Shaoxing University, Shaoxing 312000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5542-9590","authenticated-orcid":false,"given":"Changxing","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7558-5379","authenticated-orcid":false,"given":"Shigen","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China"}]},{"given":"Yuzhang","family":"Gu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem andInformation Technology, Chinese Academy of Sciences, Shanghai 200050, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Yilmaz","year":"2006","journal-title":"ACM Comp. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, M.H. (2013, January 23\u201328). Online object tracking: A benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1109\/TPAMI.2013.230","article-title":"Visual tracking: An experimental survey","volume":"36","author":"Smeulders","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","unstructured":"Grabner, H., and Bischof, H. (2006, January 17\u201322). On-line boosting and vision. Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR), New York, NY, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Forsyth, D., Torr, P., and Zisserman, A. (2008, January 12\u201318). Semi-supervised on-line boosting for robust tracking. Proceedings of the European Conference on Computer Vision (ECCV), Marseille, France.","DOI":"10.1007\/978-3-540-88688-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","article-title":"Robust object tracking with online multiple instance learning","volume":"33","author":"Babenko","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1109\/TPAMI.2014.2315808","article-title":"Fast compressive tracking","volume":"36","author":"Kaihua","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","unstructured":"Comaniciu, D., Ramesh, V., and Meer, P. (2000, January 15). Real-time tracking of non-rigid objects using mean shift. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head Island, SC, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TPAMI.2003.1195991","article-title":"Kernel-based object tracking","volume":"25","author":"Comaniciu","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1109\/TPAMI.2011.167","article-title":"Mean shift trackers with cross-bin metrics","volume":"34","author":"Leichter","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","unstructured":"Zhu, C. (2011). Video Object Tracking Using Sift and Mean Shift. [Master\u2019s Thesis, Chalmers University of Technology]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s00371-012-0677-0","article-title":"Improved mean shift integrating texture and color features for robust real time object tracking","volume":"29","author":"Bousetouane","year":"2013","journal-title":"Vis. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.patrec.2014.03.025","article-title":"Robust scale-adaptive mean-shift for tracking","volume":"49","author":"Vojir","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","unstructured":"Collins, R.T. (2003, January 18\u201320). Mean-shift blob tracking through scale space. Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR), Madison, WI, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lindeberg, T. (1994). Scale-Space Theory in Computer Vision, Kluwer Academic.","DOI":"10.1007\/978-1-4757-6465-9"},{"key":"ref_16","unstructured":"Smarandache, F. (1998). Neutrosophy: Neutrosophic Probability, Set and Logic, American Research Press."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.asoc.2014.08.066","article-title":"A novel image segmentation algorithm based on neutrosophic similarity clustering","volume":"25","author":"Guo","year":"2014","journal-title":"Appl. Soft Comp."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/978-3-319-08156-4_20","article-title":"Neutrosophic sets and fuzzy c-means clustering for improving CT liver image segmentation","volume":"303","author":"Anter","year":"2014","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1117\/1.JEI.22.1.013005","article-title":"Modified neutrosophic approach to color image segmentation","volume":"22","author":"Karabatak","year":"2013","journal-title":"J. Electron. Imag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1016\/j.sigpro.2009.10.021","article-title":"A neutrosophic approach to image segmentation based on watershed method","volume":"90","author":"Zhang","year":"2010","journal-title":"Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.measurement.2014.08.039","article-title":"A novel image thresholding algorithm based on neutrosophic similarity score","volume":"58","author":"Guo","year":"2014","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1166\/jctn.2016.4896","article-title":"A review on the applications of neutrosophic sets","volume":"13","author":"Metwally","year":"2016","journal-title":"J. Comput. Theor. Nanosci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.asoc.2015.07.025","article-title":"A novel 3D skeleton algorithm based on neutrosophic cost function","volume":"36","author":"Guo","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.3233\/JIFS-152381","article-title":"A novel object tracking algorithm by fusing color and depth information based on single valued neutrosophic cross-entropy","volume":"32","author":"Hu","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2710","DOI":"10.1016\/j.patcog.2015.02.018","article-title":"NCM: Neutrosophic c-means clustering algorithm","volume":"48","author":"Guo","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s00521-015-1891-2","article-title":"Topsis method for multi-attribute group decision-making under single-valued neutrosophic environment","volume":"27","author":"Biswas","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1142\/S1793005714500070","article-title":"A neutrosophic multi-criteria decision making method","volume":"10","author":"Kharal","year":"2014","journal-title":"New Math. Nat. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1016\/j.apm.2013.07.020","article-title":"Single valued neutrosophic cross-entropy for multicriteria decision making problems","volume":"38","author":"Ye","year":"2014","journal-title":"Appl. Math. Model."},{"key":"ref_29","first-page":"97","article-title":"Neutrosophic sets and its applications to decision making","volume":"19","author":"Majumdar","year":"2015","journal-title":"Adapt. Learn. Optim."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1080\/03081079.2012.761609","article-title":"Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment","volume":"42","author":"Ye","year":"2013","journal-title":"Int. J. Gen. Syst."},{"key":"ref_31","first-page":"410","article-title":"Single valued neutrosophic sets","volume":"4","author":"Wang","year":"2010","journal-title":"Multisp. Multistruct."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/4\/122\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:46:29Z","timestamp":1760208389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/4\/122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,2]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["info8040122"],"URL":"https:\/\/doi.org\/10.3390\/info8040122","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2017,10,2]]}}}