An index and retrieval framework integrating perceptive features and semantics for multimedia databases

doi:10.1007/s11042-008-0235-y

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	An index and retrieval framework integrating perceptive features and semantics for multimedia databases
	Shi, Zhiping; He, Qing; Shi, Zhongzhi
刊名	MULTIMEDIA TOOLS AND APPLICATIONS
	2009-04-01
卷号	42 期号:2 页码:207-231
关键词	CBMR High-dimensional index Semantics Relevance feedback
ISSN号	1380-7501
DOI	10.1007/s11042-008-0235-y
英文摘要	Typically, in multimedia databases, there exist two kinds of clues for query: perceptive features and semantic classes. In this paper, we propose a novel framework for multimedia databases index and retrieval integrating the perceptive features and semantic classes to improve the speed and the precision of the content-based multimedia retrieval (CBMR). We develop a semantics supervised clustering based index approach (briefly as SSCI): the entire data set is divided hierarchically into many clusters until the objects within a cluster are not only close in the perceptive feature space but also within the same semantic class, and then an index term is built for each cluster. Especially, the perceptive feature vectors in a cluster are organized adjacently in disk. So the SSCI-based nearest-neighbor (NN) search can be divided into two phases: first, the indexes of all clusters are scanned sequentially to get the candidate clusters with the smallest distances from the query example; second, the original feature vectors within the candidate clusters are visited to get search results. Furthermore, if the results are not satisfied, the SSCI supports an effective relevance feedback (RF) search: users mark the positive and negative samples regarded a cluster as unit instead of a single object; then the Bayesian classifiers on perceptive features and that on semantics are used respectively to adjust retrieval similarity distance. Our experiments show that SSCI-based searching was faster than VA(+)-based searching; the quality of the search result based on SSCI was better than that of the sequential search in terms of semantics; and a few cycles of the RF by the proposed approach can improve the retrieval precision significantly.
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000263918300004
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/11574]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shi, Zhiping
作者单位	Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shi, Zhiping,He, Qing,Shi, Zhongzhi. An index and retrieval framework integrating perceptive features and semantics for multimedia databases[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2009,42(2):207-231.
APA	Shi, Zhiping,He, Qing,&Shi, Zhongzhi.(2009).An index and retrieval framework integrating perceptive features and semantics for multimedia databases.MULTIMEDIA TOOLS AND APPLICATIONS,42(2),207-231.
MLA	Shi, Zhiping,et al."An index and retrieval framework integrating perceptive features and semantics for multimedia databases".MULTIMEDIA TOOLS AND APPLICATIONS 42.2(2009):207-231.