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  • @techreport{MMS2000b,
    	vgclass =	{report},
    	vgproject =	{viper,cbir},
    	author =	{Henning M\"{u}ller and Wolfgang M\"{u}ller and David McG.
    	Squire and St\'{e}phane Marchand-Maillet and Thierry Pun},
    	title =	{Long-Term Learning from User Behavior in Content-Based
    	Image Retrieval},
    	number =	{00.04},
    	institution =	{Computer Vision Group, Computing Centre, University of
    	address =	{rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve, Switzerland},
    	month =	{March},
    	year =	{2000},
    	url =	{/publications/postscript/2000/VGTR00.04_MuellerHMuellerWSquireMarchandPun.pdf},
    	url1 =	{/publications/postscript/2000/},
    	abstract =	{This article describes a simple algorithm for obtaining
    	knowledge about the importance of features from analyzing user log
    	files of a content-based image retrieval system (CBIRS).  The user log
    	files of the usage of the Viper web demonstration system are analyzed
    	over a period of four months. In this time about 3500 accesses to the
    	system were made with 800 multiple image queries.  The analysis only
    	takes into account multiple image queries of the system with positive
    	or negative input images, because only these queries contain enough
    	information for the method described in the paper.  Features frequently
    	present in images marked together positively in the same query step get
    	a higher weighting whereas features present in an image marked
    	positively and another image marked negatively in the same step get a
    	lower weighting.  The Viper system offers a very large number of simple
    	features which allows the creation of feature weightings with high
    	values for important and low values for less important features. These
    	weightings for features can of course differ for several collections
    	and as well for several users.  The results are evaluated using the
    	relevance judgments of real users and compared to the system without
    	the long-term learning.},