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  • @inproceedings{Squ1998b,
    	vgclass =	{refpap},
    	vgproject =	{cbir,viper},
    	author =	{David McG. Squire},
    	title =	{Learning a similarity-based distance measure for image
    	database organization from human partitionings of an image set},
    	booktitle =	{Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision (WACV'98)},
    	address =	{Princeton, NJ, USA},
    	pages =	{88--93},
    	month =	{October},
    	year =	{1998},
    	doi =	{},
    	url =	{/publications/postscript/1998/Squire\_wacv98.pdf},
    	url1 =	{/publications/postscript/1998/Squire\},
    	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
    	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.},