Abstract
To deal with large databases, we present a clustering based indexing technique, where the images in the database are grouped into clusters of images with similar color content using a hierarchical clustering algorithm. At search time the query image is not compared with all the images in the database, but only with a small subset. Thus the retrieval is scalable to large databases. Experiments show that this clustering based approach offers a superior response time without sacrificing the retrieval accuracy, which is crucial for large databases.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Image Processing |
| Place of Publication | Los Alamitos, CA, United States |
| Publisher | IEEE Comp Soc |
| Pages | 119-122 |
| Number of pages | 4 |
| Volume | 3 |
| State | Published - Dec 1 1998 |
| Externally published | Yes |
| Event | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA Duration: Oct 4 1998 → Oct 7 1998 |
Other
| Other | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) |
|---|---|
| City | Chicago, IL, USA |
| Period | 10/4/98 → 10/7/98 |
Fingerprint
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Electrical and Electronic Engineering
Cite this
Scalable algorithm for image retrieval by color. / Krishnamachari, Santhana; Abdel-Mottaleb, Mohamed.
IEEE International Conference on Image Processing. Vol. 3 Los Alamitos, CA, United States : IEEE Comp Soc, 1998. p. 119-122.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Scalable algorithm for image retrieval by color
AU - Krishnamachari, Santhana
AU - Abdel-Mottaleb, Mohamed
PY - 1998/12/1
Y1 - 1998/12/1
N2 - To deal with large databases, we present a clustering based indexing technique, where the images in the database are grouped into clusters of images with similar color content using a hierarchical clustering algorithm. At search time the query image is not compared with all the images in the database, but only with a small subset. Thus the retrieval is scalable to large databases. Experiments show that this clustering based approach offers a superior response time without sacrificing the retrieval accuracy, which is crucial for large databases.
AB - To deal with large databases, we present a clustering based indexing technique, where the images in the database are grouped into clusters of images with similar color content using a hierarchical clustering algorithm. At search time the query image is not compared with all the images in the database, but only with a small subset. Thus the retrieval is scalable to large databases. Experiments show that this clustering based approach offers a superior response time without sacrificing the retrieval accuracy, which is crucial for large databases.
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UR - http://www.scopus.com/inward/citedby.url?scp=0032308278&partnerID=8YFLogxK
M3 - Conference contribution
VL - 3
SP - 119
EP - 122
BT - IEEE International Conference on Image Processing
PB - IEEE Comp Soc
CY - Los Alamitos, CA, United States
ER -