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
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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. |
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