Image databases on the Web have heterogeneous characteristics since they use different similarity measures and queries are processed depending on their own schemes. In the content-based image retrieval from distributed sites, it is crucial that the metaserver has the capability to find objects, similar to a given query object in terms of the global similarity measure, from different image databases with different local similarity measures. In this paper, we investigate the problem of finding databases, which contain more objects relevant to a given query than other databases, from many image databases dispersed on the Web. This problem is referred to as a database selection problem. We propose a new selection method to determine candidate databases. The selection of databases is based on the hybrid estimator using a few sample objects and compressed histogram information of image databases. Extensive experiments on a large number of image data demonstrate that our proposed method improves the effectiveness of distributed content-based retrieval in a heterogeneous environment. (C) 2002 Elsevier Science Inc. All rights reserved.