2000
@techreport{MMS2000a,
vgclass = {report},
vgproject = {viper,cbir},
author = {Henning M\"{u}ller and Wolfgang M\"{u}ller and David McG.
Squire and Zoran Pe\u{c}enovi\'{c} and St\'{e}phane Marchand-Maillet
and Thierry Pun},
title = {An Open Framework for Distributed Multimedia Retrieval},
number = {00.03},
institution = {Computer Vision Group, Computing Centre, University of
Geneva},
address = {rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve, Switzerland},
month = {March},
year = {2000},
url = {/publications/postscript/2000/VGTR00.03_MuellerHMuellerWSquirePecenovicMarchandPun.pdf},
url1 = {/publications/postscript/2000/VGTR00.03_MuellerHMuellerWSquirePecenovicMarchandPun.ps.gz},
abstract = {This article describes a framework for distributed
multimedia retrieval which permits the connection of compliant user
interfaces with a variety of multimedia retrieval engines via an open
communication protocol, MRML (Multi Media Retrieval Markup Language).
It allows the choice of image collection, feature set and query
algorithm during run--time, permitting multiple users to query a system
adapted to their needs, using the query paradigm adapted to their
problem such as query by example (QBE), browsing queries, or query by
annotation.
User interaction is implemented over several levels and in diverse
ways. Relevance feedback is implemented using positive and negative
example images that can be used for a best--match QBE query. In
contrast, browsing methods try to ap proach the searched image by
giving overviews of the entire collection and by successive
refinements. In addition to these query methods, Long term off line
learning is implemented. It allows feature preferences per user, user
domain or over all users to be learned automatically.
We present the Viper multimedia retrieval system as the core of the
framework and an example of an MRML-compliant search engine. Viper
uses techniques adapted from traditional information retrieval (IR) to
retrieve multimedia documents, thus benefiting from the many years of
IR research. As a result, textual and visual features are treated in
the same way, facilitating true multimedia retrieval.
The MRML protocol also allows other applications to make use of the
search engi nes. This can for example be used for the design of a
benchmark test suite, querying several search engines in the same way
and comparing the results. This is motivated by the fact that the
content--based image retrieval community really lacks such a benchmark
as it already exists in text retrieval.},
}