Search results for key=MMM2000a : 1 match found.

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  • @inproceedings{MMM2000a,
    	vgclass =	{refpap},
    	vgproject =	{viper,cbir},
    	author =	{Henning M\"{u}ller and Wolfgang M\"{u}ller and
    	St\'{e}phane Marchand-Maillet and Thierry Pun and David McG.\ Squire},
    	title =	{Strategies for positive and negative relevance feedback in
    	image retrieval},
    	booktitle =	{Proceedings of the 15th International Conference on
    	Pattern Recognition},
    	address =	{Barcelona, Spain},
    	month =	{September~3--8},
    	year =	{2000},
    	doi =	{},
    	url =	{/publications/postscript/2000/MuellerHMuellerWMarchandPunSquire_icpr2000.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.},