Case study: Cairo---a distributed image retrieval system for cluster architectures
Distributed multimedia databases
A Prototype for a Distributed Image Retrieval System
HPCN Europe 2001 Proceedings of the 9th International Conference on High-Performance Computing and Networking
Retrieval of Multispectral Satellite Imagery on Cluster Architectures (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Efficient Dynamic Image Retrieval Using the Á Trous Wavelet Transformation
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Study of content-based image retrieval using parallel computing technique
CHINA HPC '07 Proceedings of the 2007 Asian technology information program's (ATIP's) 3rd workshop on High performance computing in China: solution approaches to impediments for high performance computing
On parallel image retrieval with dynamically extracted features
Parallel Computing
Efficient Grid-Based Video Storage and Retrieval
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
On-line content-based image retrieval system using joint querying and relevance feedback scheme
WSEAS Transactions on Computers
Video shot extraction on parallel architectures
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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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.In this paper 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 in the last part of this paper.