Search results for key=MSM1999a : 1 match found.

Search my entire BibTeX database
Output format: Text
BibTeX entry
     Combine using:

Abstract icon Abstract BibTeX icon BibTeX entry Postscript icon Postscript PDF icon PDF PPT icon Powerpoint

Technical Reports


  • @techreport{MSM1999a,
    	vgclass =	{report},
    	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},
    	number =	{99.03},
    	institution =	{Computer Vision Group, Computing Centre, University
    	of Geneva},
    	address =	{rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve,
    	month =	{July},
    	year =	{1999},
    	url =	{/publications/postscript/1999/VGTR99.03_MuellerSquireMuellerPun.pdf},
    	url1 =	{/publications/postscript/1999/},
    	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.},