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

1996

@inproceedings{CMO1996,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Ingemar J. Cox and Matt L. Miller and Stephen M. Omohundro
	and Peter N. Yianilos},
	title =	{Target Testing and the \texttt{PicHunter} {B}ayesian Multimedia Retrieval System},
	booktitle =	{Advances in Digital Libraries (ADL'96)},
	address =	{Library of Congress, Washington, D. C.},
	pages =	{66--75},
	month =	{May~13--15},
	year =	{1996},
	url =	{ftp://ftp.nj.nec.com/pub/ingemar/papers/adl96.ps},
	abstract =	{This paper addresses how the effectiveness of a
	content-based, multimedia information retrieval system can be measured,
	and how such a system should best use response feedback in performing
	searches. We propose a simple, quantifiable measure of an image
	retrieval system's effectiveness, ``target testing'', in which
	effectiveness is measured as the average number of images that a user
	must examine in searching for a given random target. We describe an
	initial version of \texttt{PicHunter}, a retrieval system designed to
	test a novel approach to relevance-feedback. This approach is based on
	a Bayesian framework that incorporates an explicit model of the user's
	selection process. \texttt{PicHunter} is intentionally designed to have
	a minimal, ``queryless'' user interface, so that its performance
	reflects only the performance of the relevance feedback algorithm. The
	algorithm, however, can easily be incorporated into more traditional,
	query-based systems. Employing no explicit query, and only a small
	amount of image processing, PicHunter is able to locate randomly
	selected targets in a database of 4522 images after displaying an
	average of only 55 groups of 4 images. This is more than 10 times
	better than random chance.},
}