Search results for key=MMM2001a : 1 match found.

2001

• @inproceedings{MMM2001a,
  	vgclass =	{refpap},
  	vgproject =	{viper,cbir},
  	author =	{Henning M\"uller and Wolfgang M\"uller and St\'ephane
  	Marchand-Maillet and David McG.\ Squire and Thierry Pun},
  	title =	{A web-based evaluation system for content-based image
  	retrieval},
  	booktitle =	{Proceedings of the 3rd International Workshop on
  	Multimedia Information Retrieval (in conjunction with ACM Multimedia
  	2001)},
  	address =	{Ottawa, Canada},
  	pages =	{50--54},
  	month =	{October~5},
  	year =	{2001},
  	doi =	{http://dx.doi.org/10.1145/500933.500949},
  	url =	{/publications/postscript/2001/MuellerHMuellerWMarchandSquirePun_acmmir2001.pdf},
  	url1 =	{/publications/postscript/2001/MuellerHMuellerWMarchandSquirePun_acmmir2001.ps.gz},
  	abstract =	{This papers describes a benchmark test for content-based
  	image retrieval systems (CBIRSs) with the query by example (QBE) query
  	paradigm. This benchmark is accessible via the Internet and thus allows
  	to evaluate any image retrieval system which is compliant with the
  	Multimedia Markup Language (MRML) for query formulation and result
  	transmission. Thus it allows a quick and easy comparison between
  	different features and algorithms for CBIRSs.  The benchmark is not
  	only based on a standardized communication protocol to do the
  	communication between the benchmark server and the benchmarked system,
  	but it also uses a freely downloadable image database for the
  	evaluation to make the results reproducible. A CBIR system that uses
  	MRML and other components to develop MRML-based applications can be
  	downloaded free of charge as well. The evaluation is based on several
  	queries and known relevance sets for these queries. Several answer sets
  	for the same query image are possible if user judgments of several
  	users exist, thus almost any sort of user judgment can be incorporated
  	into the system. The final results are averaged over all the queries.
  	The evaluation of several steps of relevance feedback based on the
  	collected relevance judgments is also included into the benchmark. The
  	performance of relevance feedback is often regarded to be even more
  	important than the performance in the first query step because only
  	with relevance feedback the adaptation of the system to the users
  	subjective goal can be measured.  For the evaluation of a system with
  	relevance feedback, the same evaluation measures are used on the query
  	results as for the first query step.},
  }