Title: Image database categorisation using robust modelling of finite generalised Dirichlet mixture

Authors: M. Maher Ben Ismail; H. Frigui

Addresses: Multimedia Research Laboratory, CECS Department, University of Louisville, 40292, USA ' Multimedia Research Laboratory, CECS Department, University of Louisville, 40292, USA

Abstract: We propose a novel image database categorisation approach using Robust Modelling of finite Generalised Dirichlet Mixture (RM-GDM). The proposed algorithm is based on optimising an objective function that associates two types of memberships with each data sample. The first one is the posterior probability and indicates how well a sample fits each estimated distribution. The second membership represents the degree of typicality and is used to identify and discard noise points and outliers. These properties make RM-GDM suitable for noisy and high-dimensional feature spaces. We use the RM-GDM to categorisze a large collection of colour images. Its performance is illustrated and compared to similar algorithms.

Keywords: unsupervised learning; mixture models; feature weighting; generalised Dirichlet mixture; image database categorisation; robust modelling; colour images; classification.

DOI: 10.1504/IJSISE.2012.047787

International Journal of Signal and Imaging Systems Engineering, 2012 Vol.5 No.2, pp.143 - 153

Received: 12 Sep 2011
Accepted: 12 Jan 2012
Published online: 31 Dec 2014 *

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