Search results for key=Squ1998a : 1 match found.

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.},
  }