Search results for key=MMS2000b : 1 match found.

2000

• @techreport{MMS2000b,
  	vgclass =	{report},
  	vgproject =	{viper,cbir},
  	author =	{Henning M\"{u}ller and Wolfgang M\"{u}ller and David McG.
  	Squire and St\'{e}phane Marchand-Maillet and Thierry Pun},
  	title =	{Long-Term Learning from User Behavior in Content-Based
  	Image Retrieval},
  	number =	{00.04},
  	institution =	{Computer Vision Group, Computing Centre, University of
  	Geneva},
  	address =	{rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve, Switzerland},
  	month =	{March},
  	year =	{2000},
  	url =	{/publications/postscript/2000/VGTR00.04_MuellerHMuellerWSquireMarchandPun.pdf},
  	url1 =	{/publications/postscript/2000/VGTR00.04_MuellerHMuellerWSquireMarchandPun.ps.gz},
  	abstract =	{This article describes a simple algorithm for obtaining
  	knowledge about the importance of features from analyzing user log
  	files of a content-based image retrieval system (CBIRS).  The user log
  	files of the usage of the Viper web demonstration system are analyzed
  	over a period of four months. In this time about 3500 accesses to the
  	system were made with 800 multiple image queries.  The analysis only
  	takes into account multiple image queries of the system with positive
  	or negative input images, because only these queries contain enough
  	information for the method described in the paper.  Features frequently
  	present in images marked together positively in the same query step get
  	a higher weighting whereas features present in an image marked
  	positively and another image marked negatively in the same step get a
  	lower weighting.  The Viper system offers a very large number of simple
  	features which allows the creation of feature weightings with high
  	values for important and low values for less important features. These
  	weightings for features can of course differ for several collections
  	and as well for several users.  The results are evaluated using the
  	relevance judgments of real users and compared to the system without
  	the long-term learning.},
  }