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A Distributed Image-Database Architecture for Efficient Insertion and Retrieval Tasks
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| Title |
A Distributed Image-Database Architecture for Efficient Insertion and Retrieval Tasks |
| Author(s) |
R. Weber, H.-J. Schek |
| Type |
Article |
| Booktitle |
In Fifth International Workshop on Multimedia Information Systems (MIS'99), Indian Wells, Palm Springs Desert, California, USA |
| Organization |
Institute for Information Systems, ETH Zurich |
| Month |
October
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| Year |
1999 |
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Abstract
Allowing for content-based image retrieval, an image database must
perform a number of time-consuming feature extraction tasks when
inserting a new image, when searching with a newly entered image, or
when bulk-loading many images in parallel. The duration of this
preprocessing step typically lies far beyond what one would accept
for conventional (short-running) database transactions. Hence, a
natural step is to export the extraction tasks from the database and
to distribute them among a number of external components in a
cluster. As a consequence the database plays the role of a
coordinator that assigns tasks to "its" auxiliary components. Since
the execution times for feature extraction and the availabilities of
such components vary over time, the coordinator needs additional
information about the components in order to "optimally" assign
extraction tasks to available components such that the overall
preprocessing cost is minimized. At the same time, similarity search
should be supported with high priority. In this paper, we present a
coordination middleware (CoMid) on top of a distributed,
component-based architecture that minimizes the cost of the
preprocessing step and optimally assigns extraction tasks. We provide
a solution for a dynamic environment: Any time a new feature
extraction component can be added and any time a component can be
switched off. Insertions as well as searches will not need to be
restarted. The coordination middleware takes care of such "failures"
and initiates "forward recovery". We describe in detail what data on
the components and what image meta-data are necessary for the
coordination, and how to exploit them. We report on an evaluation of
this architecture using our prototype system applied to a very large
image collection.
You can directly download a postscript (395 KB) version of this paper.
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