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dc.contributor.authorMadden, Michael G.en
dc.contributor.authorMunroe, Daniel T.en
dc.date.accessioned2009-05-15T10:16:29Zen
dc.date.available2009-05-15T10:16:29Zen
dc.date.issued2005en
dc.identifier.citation"Multi-Class and Single-Class Classification Approaches to Vehicle Model Recognition from Images", Daniel Munroe and Michael G. Madden. Proceedings of AICS-05: Irish Conference on Artificial Intelligence and Cognitive Science, Portstewart, Sept 2005.en
dc.identifier.urihttp://hdl.handle.net/10379/191en
dc.description.abstractThis paper investigates the use of machine learning classification techniques applied to the task of recognising the make and model of vehicles. Although a number of vehicle classification systems already exist, most of them seek only to distinguish between vehicle categories, e.g. identifying whether a vehicle is a bus, truck or car. The system presented here demonstrates that a set of features extracted from the frontal view of a vehicle may be used to determine the vehicle type (make and model) with high accuracy. The performance of some standard multi-class classification algorithms is compared for this problem. A one-class k-Nearest Neighbour classification algorithm is also implemented and tested.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectMachine learning classification techniquesen
dc.subjectVehicle classification systemsen
dc.subjectClassification algorithmsen
dc.subject.lcshMachine learningen
dc.subject.lcshAlgorithmsen
dc.titleMulti-Class and Single-Class Classification Approaches to Vehicle Model Recognition from Imagesen
dc.typeConference Paperen
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland