SEMI-SUPERVISED IMAGE SEGMENTATION COMBINING SSFCM AND RANDOM WALKS

摘要：

　　We present a semi-supervised image segmentation algorithm to segment the noisy image that includes a large amount of objects with the same color features.It models the images color feature through SSFCM based labeled data,and then it defines a reliability function based upon the membership calculated by SSFCM,and the pixels are classified as two types that are considered as labeled and unlabeled pixels of Random Walks,at last it performs Random Walks to produce the final segmentation.The experimental results show the effectiveness of our algorithm.It not only reduces the noise sensitivity of SSFCM but also avoids cumbersome operations that the user labels the seed points of all objects for Random Walks.

关键词： Semi-supervised image segmentation SSFCM Random walks

作者: Shengguo Chen Zhengxing Sun Jie Zhou Yi Li

作者单位: State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093,China

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 227-232

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）