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  • @inproceedings{MMM2001,
    	vgclass =	{refpap},
    	vgproject =	{viper,cbir},
    	author =	{Henning M\"uller and Wolfgang M\"uller and St\'ephane
    	Marchand-Maillet and Thierry Pun and David McG.\ Squire},
    	title =	{Automated benchmarking in content-based image retrieval},
    	booktitle =	{Proceedings of the IEEE International Conference on
    	Multimedia and Expo (ICME2001)},
    	address =	{Tokyo, Japan},
    	pages =	{1143--1146},
    	month =	{October~22--25},
    	year =	{2001},
    	doi =	{},
    	url =	{/publications/postscript/2001/MuellerHMuellerWMarchandPunSquire_icme2001.pdf},
    	url1 =	{/publications/postscript/2001/},
    	abstract =	{Benchmarking has always been a crucial problem in
    	content-based images retrieval systems (CBIRSs). A key issue is the
    	lack of a common access method to retrieval systems, such as SQL for
    	relational databases. The Multimedia Retrieval Mark-up Language (MRML),
    	described in this article, solves this problem by standardizing access
    	to CBIRSs. Other difficult problems are also addressed, such as
    	obtaining relevance judgments and choosing a database for performance
    	comparison.  We present fully automated benchmark for CBIRSs based on
    	MRML, which can be adapted to any image database and almost any kind of
    	relevance judgment. The test evaluates the performance of positive and
    	negative relevance feedback, which can be generated automatically from
    	the relevance judgments.  To illustrate our purpose, a freely
    	available, non-copyright collection is used to evaluate our CBIRS,
    	Viper.  All scripts described here are freely available for download.},