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

2004

@article{GiR2004,
	vgclass =	{refpap},
	author =	{Giorgio Giacinto and Fabio Roli},
	title =	{Bayesian relevance feedback for content-based image
	retrieval},
	journal =	{Pattern Recognition},
	volume =	{37},
	number =	{7},
	pages =	{1499--1508},
	month =	{July},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/j.patcog.2004.01.005},
	abstract =	{Despite the efforts to reduce the so-called semantic gap
	between the user's perception of image similarity and the feature-based
	representation of images, the interaction with the user remains
	fundamental to improve performances of content-based image retrieval
	systems. To this end, relevance feedback mechanisms are adopted to
	refine image-based queries by asking users to mark the set of images
	retrieved in a neighbourhood of the query as being relevant or not. In
	this paper, the Bayesian decision theory is used to estimate the
	boundary between relevant and non-relevant images. Then, a new query is
	computed whose neighbourhood is likely to fall in a region of the
	feature space containing relevant images. The performances of the
	proposed query shifting method have been compared with those of other
	relevance feedback mechanisms described in the literature. Reported
	results show the superiority of the proposed method.},
}