{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T09:11:37Z","timestamp":1685351497346},"reference-count":0,"publisher":"National Library of Serbia","issue":"4","license":[{"start":{"date-parts":[[2012,1,1]],"date-time":"2012-01-01T00:00:00Z","timestamp":1325376000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2012]]},"abstract":"<jats:p>The effectiveness of K-means clustering algorithm for image segmentation has\n   been proven in many studies, but is limited in the following problems: 1)\n   the determination of a proper number of clusters. If the number of clusters\n   is determined incorrectly, a good-quality segmented image cannot be\n   guaranteed; 2) the poor typicality of clustering prototypes; and 3) the\n   determination of an optimal number of pixels. The number of pixels plays an\n   important role in any image processing, but so far there is no general and\n   efficient method to determine the optimal number of pixels. In this paper, a\n   grid-based K-means algorithm is proposed for image segmentation. The\n   advantages of the proposed algorithm over the existing K-means algorithm\n   have been validated by some benchmark datasets. In addition, we further\n   analyze the basic characteristics of the algorithm and propose a general\n   index based on maximizing grey differences between investigated objective\n   grays and background grays. Without any additional condition, the proposed\n   index is robust in identifying an optimal number of pixels. Our experiments\n   have validated the effectiveness of the proposed index by the image results\n   that are consistent with the visual perception of the datasets.<\/jats:p>","DOI":"10.2298\/csis120126052s","type":"journal-article","created":{"date-parts":[[2012,12,28]],"date-time":"2012-12-28T10:31:33Z","timestamp":1356690693000},"page":"1679-1696","source":"Crossref","is-referenced-by-count":4,"title":["Application of grid-based k-means clustering algorithm for optimal image processing"],"prefix":"10.2298","volume":"9","author":[{"given":"Tingna","family":"Shi","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China"}]},{"given":"Penglong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China"}]},{"given":"Jeenshing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan"}]},{"given":"Shihong","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:30:48Z","timestamp":1685349048000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141200052S"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012]]}},"URL":"https:\/\/doi.org\/10.2298\/csis120126052s","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012]]}}}