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.},
}