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|Title:||A non-expert organised visual database: a case study in using the Amazon metric to search images|
|Author:||Wyeld, Theodor G.|
|Citation:||Proceedings of the International Conference on Information Visualisation, 2007; Article number 4272016:431-435|
|Conference Name:||Information Visualization 2007. Conference (11th : 2007 : Zurich, Switzerland)|
|School/Discipline:||School of Humanities : Media|
|Abstract:||In a previous paper the notion of "using the Amazon metric to construct an image database based on what people do, not what they say" was introduced (see ). In that paper we described a case study setting where 20 participants were asked to arrange a collection of 60 images from most to least similar. We found they organised them in many different ways for many different reasons. Using Wexelblat's  semantic dimensions as axes for visualisation in conjunction with the Amazon metric we were able to identify common clusters of images according to expert and non-expert orderings. This second study describes the construction of a visual database based on the results of the first case study's non-expert participants' organising strategies and rationales. The same participants from the first study were invited to search for "remembered' images in the visual database. A better understanding was gained of their detailed reasonings behind their choices. This led to the development of a non-expert organised visual database that proved to be useful to the non-expert user. This paper concludes with some recommendations for future research into developing a non-expert, selforganising, visual, image database using multiple thesauri, based on these core studies.|
|Keywords:||3D visual database; Amazon metric; Image database; Non-expert; Qualitative|
|Rights:||© 2008 IEEE|
|Appears in Collections:||Media Studies publications|
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