Image database categorisation using robust modelling of finite generalised Dirichlet mixture
by M. Maher Ben Ismail; H. Frigui
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 5, No. 2, 2012

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.

Online publication date: Wed, 31-Dec-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com