Automatic detection of image orientation using Support Vector Machines

Walsh, Dane A. (2002-12)

Thesis (MSc)--University of Stellenbosch, 2002.


ENGLISH ABSTRACT: In this thesis, we present a technique for the automatic detection of image orientation using Support Vector Machines (SVMs). SVMs are able to handle feature spaces of high dimension and automatically choose the most discriminative features for classification. We investigate the use of various kernels, including heavy tailed RBF kernels. We compare the classification performance of SVMs with the performance of multilayer perceptrons and a Bayesian classifier. Our results show that SVMs out perform both of these methods in the classification of individual images. We also implement an application for the classification of film rolls in a photographic workflow environment with 100% classification accuracy.

AFRIKAANSE OPSOMMING: In hierdie tesis, gebruik ons 'n tegniek vir die automatiese klassifisering van beeldoriëntasie deur middel van Support Vector Machines (SVM's). SVM's kan kenmerkruimtes van 'n hoë dimensie hanteer en kan automaties die mees belangrike kenmerke vir klassifikasie kies. Ons vors die gebruik van verskeie kerne, insluitende RBF-kerne, na. Ons vergelyk die klassifiseringsresultate van SVM's met die van multilaagperseptrone en 'n Bayes-klassifiseerder. Ons bewys dat SVM's beter resultate gee as beide van hierdie metodes vir die klassifikasie van individuele beelde. Ons implementeer ook a toepassing vir die klassifisering van rolle film in a fotografiese werkvloei-omgewing met 100% klassifikasie akuraatheid.

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