Adaptive relevance feedback based on Bayesian inference for image retrieval

doi:10.1016/j.sigpro.2004.10.006

	Adaptive relevance feedback based on Bayesian inference for image retrieval
	Duan, LJ ; Gao, W ; Zeng, W ; Zhao, DB
	2005-02-01
发表期刊	SIGNAL PROCESSING
ISSN	0165-1684
卷号	85 期号:2 页码:395-399
摘要	Relevance feedback can be considered as a Bayesian classification problem. For retrieving images efficiently, an adaptive relevance feedback approach based on the Bayesian inference, rich get richer (RGR), is proposed. If the feedback images in current iteration are consistent with the previous ones, the images that are similar to the query target are assigned to high probabilities. Therefore, the images that are similar to the user's ideal target are emphasized step by step. The experiments showed that the average precision of RGR improves 5-20% on each interaction compared with non-RGR. When compared with MARS, the proposed approach greatly reduces the user's efforts for composing a query and captures user's intention efficiently. (C) 2004 Elsevier B.V. All rights reserved.
关键词	relevance feedback Bayesian inference image retrieval
DOI	10.1016/j.sigpro.2004.10.006
收录类别	SCI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000226430700010
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/9970
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Duan, LJ
作者单位	1.Beijing Univ Technol, Coll Comp Sci, Beijing 100022, Peoples R China 2.Chinese Acad Sci, Comp Technol Inst, Beijing 100080, Peoples R China 3.Harbin Inst Technol, Dept Comp Sci, Harbin 150001, Peoples R China
推荐引用方式 GB/T 7714	Duan, LJ,Gao, W,Zeng, W,et al. Adaptive relevance feedback based on Bayesian inference for image retrieval[J]. SIGNAL PROCESSING,2005,85(2):395-399.
APA	Duan, LJ,Gao, W,Zeng, W,&Zhao, DB.(2005).Adaptive relevance feedback based on Bayesian inference for image retrieval.SIGNAL PROCESSING,85(2),395-399.
MLA	Duan, LJ,et al."Adaptive relevance feedback based on Bayesian inference for image retrieval".SIGNAL PROCESSING 85.2(2005):395-399.