Technical Reports

1999

@techreport{GeM1999,
	vgclass =	{report},
	vgproject =	{cbir},
	author =	{Donald Geman and Roland Moquet},
	title =	{A Stochastic Feedback Model for Image Retrieval},
	type =	{Technical report},
	institution =	{Ecole Polytechnique},
	address =	{91128 Palaiseau Cedex, France},
	year =	{1999},
	url =	{http://www.math.umass.edu/\~{}geman/Papers/rfia.ps.gz},
	abstract =	{Our goal is an efficient algorithm for image retrieval
	based on relevance feedback. We assume the user is searching for a
	particular image in a database and responds to a sequence of machine
	queries by declaring which of two (or more) displayed images is
	``closest'' to his target. Efficiency is measured by the average number
	of queries necessary to locate the image. We introduce a Bayesian
	feedback model which accounts for considerable variation in the
	responses of the user through a sequence of independent random metrics
	on feature space whose distribution may depend on both the displayed
	images and the target. Each new query is chosen to minimize the
	expected conditional entropy of the distribution over targets given the
	previous responses. The resulting algorithm is demonstrated for shape
	and image retrieval and its performance compared with theoretical
	bounds and previous models.},
}