{"title":"Application of grid-based C-means clustering algorithm for image segmentation","authors":"Shihong Yue, Jian Pan, Lijun Cui","doi":"10.1109/ICSAI.2012.6223585","DOIUrl":null,"url":null,"abstract":"C-means clustering algorithms have proven effective for image segmentation, but are limited by the following aspects: 1) the determination of a priori number of clusters. If the number of clusters can be incorrectly determined, a good-quality segmented image cannot be assured; 2) the poor real-time performances due to great time-consuming, and 3) the poor typicality of each cluster represented by the clustering prototype. In this paper, a grid-based C-means algorithm is applied to image segmentation, whose advantages over the existing C-means algorithm have demonstrated in some typical datasets. The convergence domain of the grid-based C-means algorithm has further been analyzed as well. Experiments show that the grid-based C-means algorithm outperforms the original C-means algorithm in some typical image segmentation applications.","PeriodicalId":164945,"journal":{"name":"2012 International Conference on Systems and Informatics (ICSAI2012)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Systems and Informatics (ICSAI2012)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI.2012.6223585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
C-means clustering algorithms have proven effective for image segmentation, but are limited by the following aspects: 1) the determination of a priori number of clusters. If the number of clusters can be incorrectly determined, a good-quality segmented image cannot be assured; 2) the poor real-time performances due to great time-consuming, and 3) the poor typicality of each cluster represented by the clustering prototype. In this paper, a grid-based C-means algorithm is applied to image segmentation, whose advantages over the existing C-means algorithm have demonstrated in some typical datasets. The convergence domain of the grid-based C-means algorithm has further been analyzed as well. Experiments show that the grid-based C-means algorithm outperforms the original C-means algorithm in some typical image segmentation applications.