Large image collections such as web-based image databases are being built in various locations. Because of the diversity of such image data collections, clustering images becomes an important and non-trivial problem. Such clustering tries to find the densely populated regions in the feature space to be used for efficient image retrieval. In this paper, we present an automatic clustering and querying (ACQ) approach for large image databases. Our approach can efficiently detect clusters of arbitrary shape. It does not require the number of clusters to be known a priori and is insensitive to the noise (outliers) and the order of input data. Based on this clustering approach, efficient image querying is supported. Experiments demonstrate the effectiveness and efficiency of the approach.
|Original language||English (US)|
|Number of pages||1|
|Journal||Proceedings - International Conference on Data Engineering|
|State||Published - Jan 1 2000|
All Science Journal Classification (ASJC) codes
- Engineering (miscellaneous)