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Technical Reports


  • @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,
    	month =	{April},
    	year =	{1997},
    	url =	{/publications/postscript/1997/VGTR97.03_SquirePun.pdf},
    	url1 =	{/publications/postscript/1997/},
    	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