Search results for key=SqP1997b : 1 match found.

Search my entire BibTeX database
Output format: Text
BibTeX entry
     Combine using:

Abstract icon Abstract BibTeX icon BibTeX entry Postscript icon Postscript PDF icon PDF PPT icon Powerpoint

Technical Reports


  • @techreport{SqP1997b,
    	vgclass =	{report},
    	vgproject =	{viper,cbir},
    	author =	{David McG. Squire and Thierry Pun},
    	title =	{Using human partitionings of an image set to learn a
    	similarity-based distance measure},
    	number =	{97.06},
    	institution =	{Computer Vision Group,  Computing Centre, University
    	of Geneva},
    	address =	{rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve,
    	month =	{November},
    	year =	{1997},
    	url =	{/publications/postscript/1997/VGTR97.06_SquirePun.pdf},
    	url1 =	{/publications/postscript/1997/},
    	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. 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