Abstract
In this paper, we propose a unified framework, called Markov Model Mediator (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
| Original language | English |
|---|---|
| Title of host publication | ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia |
| Pages | 372-375 |
| Number of pages | 4 |
| State | Published - Dec 1 2004 |
| Event | ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia - New York, NY, United States Duration: Oct 10 2004 → Oct 16 2004 |
Other
| Other | ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 10/10/04 → 10/16/04 |
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Keywords
- Content-based Image Retrieval
- Image Database Clustering
- Markov Model Mediators (MMMs)
ASJC Scopus subject areas
- Engineering(all)
Cite this
Affinity relation discovery in image database clustering and content-based retrieval. / Shyu, Mei-Ling; Chen, Shu Ching; Chen, Min; Zhang, Chengcui.
ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia. 2004. p. 372-375.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Affinity relation discovery in image database clustering and content-based retrieval
AU - Shyu, Mei-Ling
AU - Chen, Shu Ching
AU - Chen, Min
AU - Zhang, Chengcui
PY - 2004/12/1
Y1 - 2004/12/1
N2 - In this paper, we propose a unified framework, called Markov Model Mediator (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
AB - In this paper, we propose a unified framework, called Markov Model Mediator (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
KW - Content-based Image Retrieval
KW - Image Database Clustering
KW - Markov Model Mediators (MMMs)
UR - http://www.scopus.com/inward/record.url?scp=13444249597&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=13444249597&partnerID=8YFLogxK
M3 - Conference contribution
SN - 1581138938
SN - 9781581138931
SP - 372
EP - 375
BT - ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
ER -