The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases
|
|
Author(s): Zhiping Shi (Institute of Computing Technology, Chinese Academy of Sciences, China), Qingyong Li (Beijing Jiaotong University, China), Qing He (Institute of Computing Technology, Chinese Academy of Sciences, China)and Zhongzhi Shi (Institute of Computing Technology, Chinese Academy of Sciences, China)
Copyright: 2009
Pages: 26
Source title:
Artificial Intelligence for Maximizing Content Based Image Retrieval
Source Author(s)/Editor(s): Zongmin Ma (Northeastern University, China)
DOI: 10.4018/978-1-60566-174-2.ch013
Purchase
|
Abstract
Semantics-based retrieval is a trend of the Content-Based Multimedia Retrieval (CBMR). Typically, in multimedia databases, there exist two kinds of clues for query: perceptive features and semantic classes. In this chapter, we proposed a novel framework for multimedia database organization and retrieval, integrating the perceptive features and semantic classes. Thereunto, a semantics supervised cluster-based index organization approach (briefly as SSCI) was developed: 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; then an index entry is built for each cluster. Especially, the perceptive feature vectors in a cluster are organized adjacently in disk. Furthermore, the SSCI supports a relevance feedback approach: users sign the positive and negative examples regarded a cluster as unit rather than a single object. Our experiments show that the proposed framework can improve the retrieval speed and precision of the CBMR systems significantly.
Related Content
|
Aswathy Ravikumar, Harini Sriraman.
© 2023.
18 pages.
|
|
Ezhilarasie R., Aishwarya N., Subramani V., Umamakeswari A..
© 2023.
10 pages.
|
|
Sangeetha J..
© 2023.
13 pages.
|
|
Manivannan Doraipandian, Sriram J., Yathishan D., Palanivel S..
© 2023.
14 pages.
|
|
T. Kavitha, Malini S., Senbagavalli G..
© 2023.
36 pages.
|
|
Uma K. V., Aakash V., Deisy C..
© 2023.
23 pages.
|
|
Alageswaran Ramaiah, Arun K. S., Yathishan D., Sriram J., Palanivel S..
© 2023.
17 pages.
|
|
|