Scheduling aspects for image retrieval in cluster-based image databases

Systems for the archival and retrieval of images are used in many areas, for example medical applications, news agencies, etc. The state-of-the-art approach for image description considers a priori extracted features. The disadvantageous reduction of the image content onto a few low-level features limits the applicability of image databases. A search for objects and other important image components requires dynamic feature extraction. The related computational and storage requirements exceed the possibilities of computer architectures with a single processing element. Therefore we developed a cluster platform, which supports the implementation of this novel retrieval approach in existing systems. We introduce the basic principles of image retrieval with dynamic feature extraction and a cluster platform. The main focus regards thereby the workload balancing across the cluster. For this purpose we developed a scheduling heuristic and executed performance measurements with the implemented prototype. The obtained results are discussed.