Conferences with full paper or abstract

1999

@inproceedings{MSM1999b,
	vgclass =	{fullconf},
	vgproject =	{viper,cbir},
	author =	{Wolfgang M\"{u}ller and David McG. Squire and Henning
	M\"{u}ller and Thierry Pun},
	title =	{Hunting moving targets: an extension to {B}ayesian methods
	in multimedia databases},
	editor =	{Sethuraman Panchanathan and Shih-Fu Chang and C.-C. Jay
	Kuo},
	booktitle =	{Multimedia Storage and Archiving Systems IV (VV02)},
	address =	{Boston, Massachusetts, USA},
	volume =	{3846},
	series =	{SPIE Proceedings},
	month =	{September~20--22},
	year =	{1999},
	note =	{(SPIE Symposium on Voice, Video and Data Communications)},
	url =	{/publications/postscript/1999/MuellerWSquireMuellerHPun\_msasIV.pdf},
	url1 =	{/publications/postscript/1999/MuellerWSquireMuellerHPun\_msasIV.ps.gz},
	abstract =	{It has been widely recognised that the difference between
	the level of abstraction of the formulation of a query (by example) and
	that of the desired result (usually an image with certain semantics)
	calls for the use of learning methods that try to bridge this gap. Cox
	\emph{et al.}\ have proposed a Bayesian method to learn the user's
	preferences during each query.

	  Cox \emph{et al.}\'s system, \texttt{PicHunter}, is designed for
	  optimal performance when the user is searching for a fixed target
	  image. The performance of the system was evaluated using target
	  testing, which ranks systems according to the number of interaction
	  steps required to find the target, leading to simple, easily
	  reproducible experiments.

	  There are some aspects of image retrieval, however, which are not
	  captured by this measure. In particular, the possibility of query
	  drift (i.e.\ a moving target) is completely ignored. The algorithm
	  proposed by Cox \emph{et al.}\ does not cope well with a change of
	  target at a late query stage, because it is assumed that user
	  feedback is noisy, but consistent.

	  In the case of a moving target, however, the feedback is noisy
	  \emph{and} inconsistent with earlier feedback.

	  In this paper we propose an enhanced Bayesian scheme which
	  selectively forgets inconsistent user feedback, thus enabling both
	  the program and the user to ``change their minds''.  The
	  effectiveness of this scheme is demonstrated in moving target tests
	  on a database of heterogeneous real-world images.},
}