Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
In search of clusters (2nd ed.)
In search of clusters (2nd ed.)
Surfimage: a flexible content-based image retrieval system
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
Image Databases Are Not Databases with Images
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
Scheduling Aspects for Image Retrieval in Cluster-Based Image Databases
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Parallel data intensive computing in scientific and commercial applications
Parallel Computing - Parallel data-intensive algorithms and applications
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The increasing use of digital images in applications ranging from remote sensing to medical applications and industrial control systems results in a demand for well-suited and efficient techniques for their storage, management and retrieval. The state-of-the-art approach for image retried considers a priori extracted features, which are compared to query information supplied by a user in the form of, for example, a list of keywords or the corresponding features of a sample image or sketch. In this paper an alternative, object-based approach for image retrieval is presented. This allows the user to specify and to search for certain regions of interest in images. The marked regions are represented by wavelet coefficients and searched in all image sections during query runtime. All other image elements are ignored, thus a detailed search can be realised. The resulting computational effort can be overcome by utilisation of pardlel architectures. An example for a cluster-bawd image database is discused in the last part of this paper.