{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:43:57Z","timestamp":1777902237108,"version":"3.51.4"},"reference-count":15,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2012,5,22]],"date-time":"2012-05-22T00:00:00Z","timestamp":1337644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2012,10]]},"abstract":"<jats:p>Much research has been conducted recently relating to ubiquitous intelligent computing. Ubiquitous intelligence-enabled techniques, such as clustering and image segmentation, have focused on the development of intelligence methodologies. In this paper, a simultaneous mode-seeking and clustering algorithm called the Generalized Transport Mean Shift (GTMS) was introduced. The data points were designated as the \u2018transporter\u2013trailer\u2019 characteristic. The important concept of transportation was used to solve the problem of redundant computations of mode-seeking algorithms. The time complexity of the GTMS algorithm is much lower than that of the Mean Shift (MS) algorithm. This means it is able to be used in a problem that has a very high data point, in particular, the segmentation of images containing the green vegetation. The proposed algorithm was tested on clustering and image-segmentation problems. The experimental results showed that the GTMS algorithm improves upon the existing algorithms in terms of both accuracy and time consumption. The GTMS algorithm\u2019s highest speed is also 333.98 times faster than that of the standard MS algorithm. The redundancy computation can be reduced by omitting more than 90% of the data points at the third iteration of the mode-seeking process. This is because GTMS algorithm mainly reduces the data in the mode-seeking process. Thus, use of the GTMS algorithm would allow for the building of an intelligent portable device for surveying green vegetables in a ubiquitous environment.<\/jats:p>","DOI":"10.1177\/0037549712445233","type":"journal-article","created":{"date-parts":[[2012,5,25]],"date-time":"2012-05-25T15:48:26Z","timestamp":1337960906000},"page":"1202-1215","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalized Transport Mean Shift algorithm for ubiquitous intelligence"],"prefix":"10.1177","volume":"88","author":[{"given":"Khamron","family":"Sunat","sequence":"first","affiliation":[{"name":"Department of Computer Science, Khon Kaen University, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panida","family":"Padungweang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Statistic and Computer, Ubon Ratchathani University, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sirapat","family":"Chiewchanwattana","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khon Kaen University, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2012,5,22]]},"reference":[{"key":"bibr1-0037549712445233","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1975.1055330"},{"key":"bibr2-0037549712445233","doi-asserted-by":"publisher","DOI":"10.1109\/34.400568"},{"key":"bibr3-0037549712445233","first-page":"1197","volume-title":"proceedings of the international conference on computer vision","author":"Comaniciu D"},{"key":"bibr4-0037549712445233","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"bibr5-0037549712445233","volume-title":"statistical methods in video processing workshop","author":"DeMenthon D"},{"key":"bibr6-0037549712445233","first-page":"153","volume-title":"proceedings of the 23rd international conference on machine learning","author":"Carreira-Perpi\u00f1\u00e1n M\u00c1"},{"key":"bibr7-0037549712445233","first-page":"181","volume-title":"proceedings of the 13th international conference on systems, signals and image processing","author":"Padungweing P"},{"key":"bibr8-0037549712445233","first-page":"1160","volume-title":"proceedings of the 2006 IEEE Computer society conference on computer vision and pattern recognition","author":"Carreira-Perpi\u00f1\u00e1n M\u00c1"},{"key":"bibr9-0037549712445233","first-page":"644","volume-title":"proceedings of the IEEE international conference on computer vision","volume":"1","author":"Yang C"},{"key":"bibr10-0037549712445233","first-page":"221","volume-title":"proceedings of the ninth SIAM international conference on data mining","author":"Yuan X-T"},{"key":"bibr11-0037549712445233","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.232"},{"key":"bibr12-0037549712445233","doi-asserted-by":"publisher","DOI":"10.1109\/34.888716"},{"key":"bibr13-0037549712445233","doi-asserted-by":"publisher","DOI":"10.1137\/0201010"},{"key":"bibr14-0037549712445233","unstructured":"Frank A, Asuncion A. UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science, http:\/\/archive.ics.uci.edu\/ml (2010). Accessed date: April 22, 2012."},{"key":"bibr15-0037549712445233","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2008.08.002"}],"container-title":["SIMULATION"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0037549712445233","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0037549712445233","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:23:49Z","timestamp":1777634629000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0037549712445233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,5,22]]},"references-count":15,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2012,10]]}},"alternative-id":["10.1177\/0037549712445233"],"URL":"https:\/\/doi.org\/10.1177\/0037549712445233","relation":{},"ISSN":["0037-5497","1741-3133"],"issn-type":[{"value":"0037-5497","type":"print"},{"value":"1741-3133","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,5,22]]}}}