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  • @inproceedings{Squ1998c,
    	vgclass =	{fullconf},
    	vgproject =	{cbir,viper},
    	author =	{David McG. Squire},
    	title =	{Using human partitionings of image sets to learn a
    	similarity-based distance measure for the organization of image
    	editor =	{C.-C. Jay Kuo and Shih-Fu Chang and Sethuraman
    	booktitle =	{Multimedia Storage and Archiving Systems III (VV02)},
    	address =	{Boston, Massachusetts, USA},
    	volume =	{3527},
    	series =	{SPIE Proceedings},
    	pages =	{80--88},
    	month =	{November},
    	year =	{1998},
    	note =	{(SPIE Symposium on Voice, Video and Data Communications)},
    	abstract =	{In this paper our goal is to employ human judgments of
    	image similarity to improve the organization of an image database for
    	content-based retrieval. We first derive a statistic, $\kappa_B$ for
    	measuring the agreement between two partitionings of an image set into
    	unlabeled subsets. This measure can be used both to measure the degree
    	of agreement between pairs of human subjects, and also between human
    	and machine partitionings of an image set. It also allows a direct
    	comparison of database organizations, as opposed to the indirect
    	measure available via precision and recall measurements. This provides
    	a rigorous means of selecting between competing image database
    	organization systems, and assessing how close the performance of such
    	systems is to that which might be expected from a database organization
    	done by hand.
    	We then use the results of experiments in which human subjects are
    	asked to partition a set of images into unlabeled subsets to define a
    	similarity measure for pairs of images based on the frequency with
    	which they were judged to be similar. We show that, when this measure
    	is used to partition an image set using a clustering technique, the
    	resultant clustering agrees better with those produced by human
    	subjects than any of the feature space-based techniques investigated.
    	Finally, we investigate the use of machine learning techniques to
    	discover a mapping from a numerical feature space to this perceptual
    	similarity space. Such a mapping would allow the ground truth knowledge
    	abstracted from the human judgments to be generalized to unseen images.
    	We show that a learning technique based on an extension of a Kohonen
    	network allows a similarity space to be learnt which results in
    	partitionings in excellent agreement with those produced by human