Search results for key=MMS2000a : 1 match found.

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.},
  }