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Technical Reports


  • @techreport{MMS2000,
    	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 =	{Strategies for positive and negative relevance feedback in
    	image retrieval},
    	number =	{00.01},
    	institution =	{Computer Vision Group, Computing Centre, University of
    	address =	{rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve, Switzerland},
    	month =	{January},
    	year =	{2000},
    	url =	{/publications/postscript/2000/VGTR00.01_MuellerHMuellerWSquireMarchandPun.pdf},
    	url1 =	{/publications/postscript/2000/},
    	abstract =	{Relevance feedback has been shown to be a very
    	effective tool for enhancing retrieval results in text retrieval. In
    	content-based image retrieval it is more and more frequently used and
    	very good results have been obtained. However, too much negative
    	feedback may destroy a query as good features get negative weightings.
    	This paper compares a variety of strategies for positive and negative
    	feedback. The performance evaluation of feedback algorithms is a hard
    	problem. To solve this, we obtain judgments from several users and
    	employ an automated feedback scheme. We can then evaluate different
    	techniques using the same judgments. Using automated feedback, the
    	ability of a system to adapt to the user's needs can be measured very
    	effectively. Our study highlights the utility of negative feedback,
    	especially over several feedback steps.},