Introduction

This special issue of Multimedia Tools and Applications contains selected papers from the First International Workshop on Computer Vision meets Databases, or CVDB 2004, which was held in Paris, France, on June 13, 2004. The workshop was co-located with the 2004 ACM SIGMOD/PODS conferences. For a long time, the computer vision community has been working on contentbased multimedia retrieval. Researchers from that community aim at defining better content-based descriptors and extracting them from images. The descriptors obtained are often represented as points in multi-dimensional spaces and some metrics are used during similarity retrieval. Their focus is on increasing the recognition power of their schemes and they usually evaluate their strength using data sets that fit in main memory because they try to avoid the secondary storage management burden. Facilitating the management of very large amounts of data and removing this disk burden has long been a strong motivation for the database community. This is particularly crucial for multimedia databases whose sizes grow very fast. Therefore, researchers in databases have proposed many smart multidimensional indexing schemes with some elegant algorithms to compute nearest-neighbor and top-n queries. Yet, it is surprising to see that only few works in the computer vision community have adopted any of these indexing schemes. A common reason evoked is that the