Boosting the Semantics Sensitive Satellite Image Retrieval Using a Voting Algorithm
This paper proposes a semantic inference approach, which utilizes a group of context-sensitive Bayesian networks to infer the semantic concepts based on different regional spatial relationships, i.e. disjoined, bordering, invaded by, surrounded by, near, far, right, left, above and below. Each Bayesian network performs the inference based on one kind of the regional spatial relationships. Finally, a voting algorithm is proposed to combine the group of the Bayesian networks into a more accurate and robust semantic concept classifier. The experiments using IKONOS imagery show that the precision of the proposed voting algorithm is consistently higher than that of the single context-sensitive Bayesian network.
semantic inference context-sensitive Bayesian network regional spatial relationship voting algorithm semantic concept classifier
Yikun Li Xiaoyuan Dong
School of Mathematics, Physics and Software Engineering, Lanzhou Jiaotong University Mailbox 504 Lanzhou Jiaotong University, Lanzhou 730070,P.R.China
国际会议
福州
英文
335-339
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）