1998
@techreport{Squ1998a,
vgclass = {report},
vgproject = {viper,cbir},
author = {David McG. Squire},
title = {Learning a similarity-based distance measure for image
database organization from human partitionings of an image set},
number = {98.03},
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 = {1998},
url = {/publications/postscript/1998/VGTR98.03_Squire.pdf},
url1 = {/publications/postscript/1998/VGTR98.03_Squire.ps.gz},
abstract = {In this paper we employ human judgments of image
similarity to improve the organization of an image database. We first
derive a statistic, $\kappa_B$ which measures the agreement between two
partitionings of an image set. $\kappa_B$ is used to assess agreement
both amongst and between human and machine partitionings. This provides
a rigorous means of choosing between competing image database
organization systems, and of assessing the performance of such systems
with respect to human judgments.
Human partitionings of an image set are used to define an similarity
value based on the frequency with which images are judged to be
similar. When this measure is used to partition an image set using a
clustering technique, the resultant partitioning agrees better with
human partitionings than any of the feature-space-based techniques
investigated.
Finally, we investigate the use multilayer perceptrons and a
\emph{Distance Learning Network} to learn a mapping from feature space
to this perceptual similarity space. The Distance Learning Network is
shown to learn a mapping which results in partitionings in excellent
agreement with those produced by human subjects.},
}