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dc.contributor.authorGale, T.M.
dc.contributor.authorSun, Yi.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2011-12-07T17:01:04Z
dc.date.available2011-12-07T17:01:04Z
dc.date.issued2005
dc.identifier.citationGale , T M , Sun , Y , Adams , R G & Davey , N 2005 , Comparing computational and human measures of visual similarity . in Procs of the 9th Neural Computation and Psychology Workshop . vol. 9 , World Scientific Publishing , pp. 397-401 .
dc.identifier.otherPURE: 487319
dc.identifier.otherPURE UUID: 12745b07-982f-473d-aa7e-33895bb22d3e
dc.identifier.otherdspace: 2299/3970
dc.identifier.urihttp://hdl.handle.net/2299/7240
dc.description"Electronic version of an article published as [Gale, T.M., Sun, Yi., Adams, R. and Davey, N. (2005) ' Comparing computational and human measures of visual similarity.' Modeling Language, Cognition and Action - Procs of the 9th Neural Computation and Psychology Workshop pp.397-401] 10.1142/9789812701886_0042 copyright World Scientific Publishing Company , Original paper can be found at: http://eproceedings.worldscinet.com/9789812701886/9789812701886_0042.html "
dc.description.abstractThere have been many attempts to quantify visual similarity within different categories of objects, with a view to using such measures to predict impaired recognition performance. Although many studies have linked measures of visual similarity to behavioral outcomes associated with object recognition, there has been little research on whether these measures are associated with human ratings of perceived similarity. In this work, we compare similarity measures extracted from Principal Component Analysis, Isometric Feature Mapping and wavelets representations with ratings of human subjects. Our results show that features extracted by calculating the standard deviation of wavelet coefficients provides the closest fit to the human rating data of all the methods we applied here.en
dc.language.isoeng
dc.publisherWorld Scientific Publishing
dc.relation.ispartofProcs of the 9th Neural Computation and Psychology Workshop
dc.titleComparing computational and human measures of visual similarityen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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