An index and retrieval framework integrating perceptive features and semantics for multimedia databases
An index and retrieval framework integrating perceptive features and semantics for multimedia databases
复制标题
集成感知特征和语义的多媒体数据库索引和检索框架
DOI：
10.1007/s11042-008-0235-y
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发表时间：
2009-04
影响因子：
3.6
通讯作者：
Shi, Zhongzhi
中科院分区：
文献类型：
--
作者：
He, Qing;Shi, Zhiping;Shi, Zhongzhi
通常，在多媒体数据库中，存在两种用于查询的线索：感知特征和语义类。为了提高基于内容的多媒体检索(CBMR)的速度和精度，提出了一种结合感知特征和语义类别的多媒体数据库索引和检索框架。我们提出了一种基于语义监督聚类的索引方法(简称SSCI)：将整个数据集分层地划分为多个簇，直到簇中的对象不仅在感知特征空间中接近，而且在同一语义类中，然后为每个簇建立一个索引项。特别是，簇中的感知特征向量被邻接地组织在磁盘上。因此，基于SSCI的最近邻搜索可以分为两个阶段：首先，依次扫描所有聚类的索引，以获得与查询实例距离最小的候选聚类；其次，访问候选聚类内的原始特征向量，以获得搜索结果。此外，如果搜索结果不满意，SSCI支持有效的相关反馈(RF)搜索：用户将正样本和负样本作为一个单元而不是单个对象进行标记；然后分别使用基于感知特征的贝叶斯分类器和基于语义的贝叶斯分类器来调整检索相似距离。实验表明，基于SSCI的搜索比基于VA+的搜索速度更快，基于SSCI的搜索结果在语义上要好于顺序搜索，并且使用该方法进行几个循环的RF可以显著提高检索精度。
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.
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DOI：
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发表时间：
1998-08
期刊：
--
影响因子：
--
作者：
Y. Ishikawa;R. Subramanya;C. Faloutsos
通讯作者：
Y. Ishikawa;R. Subramanya;C. Faloutsos
DOI：
10.1360/jos161039
发表时间：
2005
期刊：
Journal of Software
影响因子：
--
作者：
Zhi-Ping Shi;Hu Hong;Qing-Yong Li;Zhongzhi Shi;Chan-Lun Duan
通讯作者：
Zhi-Ping Shi;Hu Hong;Qing-Yong Li;Zhongzhi Shi;Chan-Lun Duan
DOI：
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发表时间：
1998-08
期刊：
--
影响因子：
--
作者：
R. Weber;H. Schek;Stephen Blott
通讯作者：
R. Weber;H. Schek;Stephen Blott
DOI：
10.1109/93.621578
发表时间：
1997-07
期刊：
IEEE Multim.
影响因子：
--
作者：
John R. Smith-;Shih-Fu Chang
通讯作者：
John R. Smith-;Shih-Fu Chang
DOI：
10.1016/j.is.2005.01.001
发表时间：
2006-09
期刊：
Inf. Syst.
影响因子：
--
作者：
H. Ferhatosmanoğlu;E. Tuncel;D. Agrawal;A. E. Abbadi
通讯作者：
H. Ferhatosmanoğlu;E. Tuncel;D. Agrawal;A. E. Abbadi