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dc.contributor.authorWyeld, Theodor
dc.contributor.authorColomb, Robert M
dc.date.accessioned2009-12-21T03:12:33Z
dc.date.available2009-12-21T03:12:33Z
dc.date.issued2006-07
dc.identifier.citationProceedings of the 10th International IEEE Conference on Information Visualisation (IV 06), London, England, United Kingdom, 05 -07 July 2006 / E. Banissi, R. A. Burkhard, A. Ursyn, J. J. Zhang, M. Bannatyne, C. Maple, A. J. Cowell, G. Y. Tian and M. Hou (eds.), pp. 74-79en
dc.identifier.isbn978-000000000
dc.identifier.urihttp://hdl.handle.net/2328/8018
dc.description.abstractCurrent image database metadata schemas require users to adopt a specific text-based vocabulary. Textbased metadata is good for searching but not for browsing. Existing image-based search facilities, on the other hand, are highly specialised and so suffer similar problems. Wexelblat's semantic dimensional spatial visualisation schemas go some way towards addressing this problem by making both searching and browsing more accessible to the user in a single interface. But the question of how and what initial metadata to enter a database remains. Different people see different things in an image and will organise a collection in equally diverse ways. However, we can find some similarity across groups of users regardless of their reasoning. For example, a search on Amazon.com returns other products also, based on an averaging of how users navigate the database. In this paper we report on applying this concept to a set of images for which we have visualised them using traditional methods and the Amazon.com method. We report on the findings of this comparative investigation in a case study setting involving a group of randomly selected participants. We conclude with the recommendation that in combination, the traditional and averaging methods would provide an enhancement to current database visualisation, searching, and browsing facilities.en
dc.language.isoen
dc.publisherIEEEen
dc.rightsCopyright © 2006 IEEE. Published version of the paper reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
dc.subjectBrowsing facilitiesen
dc.subjectImage database metadataen
dc.subjectVisualizationen
dc.titleUsing the Amazon metric to construct an image database based on what people do, not what they sayen
dc.typePresentationen
dc.identifier.doihttps://doi.org/10.1109/IV.2006.118en


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