Parallel and distributed computing for an adaptive visual object retrieval system

Computer vision and image processing has always been an active research domain that requires an enormous computational effort. In this paper we present the architecture of a modular object retrieval system. It is based on a dataflow concept which allows flexible adaptations to different tasks. This concept facilitates parallel processing as well as distributed computing. We also present a dynamic load balancing service for heterogeneous environments that has been integrated to improve system performance. First experiments show that the developed balancer performs better than standard balancing techniques in this environment.