1997
@techreport{SqP1997a,
vgclass = {report},
vgproject = {viper,cbir},
author = {David McG. Squire and Thierry Pun},
title = {Assessing Agreement Between Human and Machine Clusterings
of Image Databases},
number = {97.03},
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 = {April},
year = {1997},
url = {/publications/postscript/1997/VGTR97.03_SquirePun.pdf},
url1 = {/publications/postscript/1997/VGTR97.03_SquirePun.ps.gz},
abstract = {There is currently much interest in the organization and
\emph{content-based} querying image databases. The usual hypothesis is
that image similarity can be characterized by low-level features,
without further abstraction. This assumes that agreement between
machine and human measures of similarity is sufficient for the database
to be useful. To assess this assumption, we develop measures of the
agreement between partitionings of an image set, showing that chance
agreements \emph{must} be considered. These measures are used to
assess the agreement between human subjects and several machine
clustering techniques on an image set. The results can be used to
select and refine distance measures for querying and organizing image
databases.},
}