Abstract
With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called Markov Model Mediators (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.
| Original language | English |
|---|---|
| Title of host publication | MMDB 2004: Proceedings of the Second ACM International Workshop on Multimedia Databases |
| Editors | S.-C. Chen, M.-L. Shyu, M. Ronthaler |
| Pages | 19-27 |
| Number of pages | 9 |
| State | Published - Dec 1 2004 |
| Event | MMDB 2004: Proceedings of the Second ACM International Workshop on Multimedia Databases - Washington, DC, United States Duration: Nov 13 2004 → Nov 13 2004 |
Other
| Other | MMDB 2004: Proceedings of the Second ACM International Workshop on Multimedia Databases |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 11/13/04 → 11/13/04 |
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Keywords
- Content-based Image Retrieval (CBIR)
- Image Database Clustering
- Markov Model Mediators (MMMs)
ASJC Scopus subject areas
- Engineering(all)
Cite this
A unified framework for image database clustering and content-based retrieval. / Shyu, Mei-Ling; Chen, Shu Ching; Chen, Min; Zhang, Chengcui.
MMDB 2004: Proceedings of the Second ACM International Workshop on Multimedia Databases. ed. / S.-C. Chen; M.-L. Shyu; M. Ronthaler. 2004. p. 19-27.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A unified framework for 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 - With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called Markov Model Mediators (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.
AB - With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called Markov Model Mediators (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.
KW - Content-based Image Retrieval (CBIR)
KW - Image Database Clustering
KW - Markov Model Mediators (MMMs)
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M3 - Conference contribution
AN - SCOPUS:20444432817
SN - 1581139756
SP - 19
EP - 27
BT - MMDB 2004: Proceedings of the Second ACM International Workshop on Multimedia Databases
A2 - Chen, S.-C.
A2 - Shyu, M.-L.
A2 - Ronthaler, M.
ER -