Refereed full papers (journals, book chapters, international conferences)

2000

@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 =	{https://doi.org/10.1109/ICPR.2000.905650},
	url =	{/publications/postscript/2000/MuellerHMuellerWMarchandPunSquire_icpr2000.pdf},
	url1 =	{/publications/postscript/2000/MuellerHMuellerWMarchandPunSquire_icpr2000.ps.gz},
	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.},
}