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

1998

@inproceedings{WCT1998,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Matthew E.J. Wood and Neill W. Campbell and Barry T.
	Thomas},
	title =	{Iterative Refinement by Relevance Feedback in
	Content-Based Digital Image Retrieval},
	booktitle =	{Proceedings of The Fifth ACM International Multimedia Conference ({ACM} {M}ultimedia 98)},
	address =	{Bristol, UK},
	pages =	{13--20},
	month =	{September},
	year =	{1998},
	url =	{http://www.cs.bris.ac.uk/Tools/Reports/Abstracts/1998-wood.html},
	abstract =	{Many image-database retrieval systems rely heavily on the
	success of one -shot queries, using optimised feature sets to obtain
	the best possible results. What is often missing from this approach is
	acceptance of the fact that the user knows considerably more about the
	query being made than can be conveyed in such relatively simple terms.
	If the query fails then the user must try and improve the description
	using only the available feature descriptors.

	This paper describes how a query system can exploit the user's
	knowledge to a higher extent by employing relevance feedback to
	iteratively refine queries at run-time. Subjects of interest are chosen
	by selection of regions from pre-processed, segmented images, giving
	access to object-specific, local information which is not possible in a
	global pattern-matching approach. After an initial retrieval attempt,
	feedback is given in the form of acceptance or rejection of images
	offered. This information is used as a collection of positive and
	negative training examples for a class-specific classification network
	by identifying clusterings in the data and the spread along feature
	axes. Each network consists of a set of Radial Basis Function nodes
	with a non-linear perceptron output layer. Network training is carried
	out off- line using the data gathered during an on-line query session
	with the user. The user can review and adjust the behaviour of the
	network in the next session.  Over time, collections of these networks
	can be built into a hierarchical class database, resulting into highly
	useful retrieval tool specifically train ed for the nature of the
	user's database.},
}