Abstract
The administration of very large collections of images accentuates the classical problems of indexing and efficiently querying information. This paper describes a new method applied to very large still image databases that combines two data mining techniques: clustering and association rules mining in order to better organize image collections and to improve the performance of queries. The objective of our work is to exploit association rules discovered by mining global MPEG-7 features data and to adapt the query processing. In our experiment, we use five MPEG-7 features to describe several thousands of still images. For each feature, we initially determine several clusters of images by using a K-mean algorithm. Then, we generate association rules between different clusters of features and exploit these rules to rewrite the query and to optimize the query-by-content processing.
| Original language | English |
|---|---|
| Title of host publication | 2004 IEEE International Conference on Multimedia and Expo (ICME) |
| Pages | 1299-1302 |
| Number of pages | 4 |
| Volume | 2 |
| Publication status | Published - 1 Dec 2004 |
| Externally published | Yes |
| Event | 2004 IEEE International Conference on Multimedia and Expo (ICME) - Taipei, Taiwan, Province of China Duration: 27 Jun 2004 → 30 Jun 2004 |
Other
| Other | 2004 IEEE International Conference on Multimedia and Expo (ICME) |
|---|---|
| Country | Taiwan, Province of China |
| City | Taipei |
| Period | 27/6/04 → 30/6/04 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Multimedia indexing and retrieval with features association rules mining. / Kouomou-Choupo, Anicet; Berti-Equille, Laure; Morin, Annie.
2004 IEEE International Conference on Multimedia and Expo (ICME). Vol. 2 2004. p. 1299-1302.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multimedia indexing and retrieval with features association rules mining
AU - Kouomou-Choupo, Anicet
AU - Berti-Equille, Laure
AU - Morin, Annie
PY - 2004/12/1
Y1 - 2004/12/1
N2 - The administration of very large collections of images accentuates the classical problems of indexing and efficiently querying information. This paper describes a new method applied to very large still image databases that combines two data mining techniques: clustering and association rules mining in order to better organize image collections and to improve the performance of queries. The objective of our work is to exploit association rules discovered by mining global MPEG-7 features data and to adapt the query processing. In our experiment, we use five MPEG-7 features to describe several thousands of still images. For each feature, we initially determine several clusters of images by using a K-mean algorithm. Then, we generate association rules between different clusters of features and exploit these rules to rewrite the query and to optimize the query-by-content processing.
AB - The administration of very large collections of images accentuates the classical problems of indexing and efficiently querying information. This paper describes a new method applied to very large still image databases that combines two data mining techniques: clustering and association rules mining in order to better organize image collections and to improve the performance of queries. The objective of our work is to exploit association rules discovered by mining global MPEG-7 features data and to adapt the query processing. In our experiment, we use five MPEG-7 features to describe several thousands of still images. For each feature, we initially determine several clusters of images by using a K-mean algorithm. Then, we generate association rules between different clusters of features and exploit these rules to rewrite the query and to optimize the query-by-content processing.
UR - http://www.scopus.com/inward/record.url?scp=11244351793&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=11244351793&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:11244351793
SN - 0780386035
SN - 9780780386037
VL - 2
SP - 1299
EP - 1302
BT - 2004 IEEE International Conference on Multimedia and Expo (ICME)
ER -