A mixture density based approach to object recognition for image retrieval
Jörg Dahmen, M. O. Güld, Hermann Ney, Klaus Beulen · 2000
In the last few years, statistical classifiers based on Gaussian mixture densities proved to be very efficient for automatic speech recognition. The aim of this paper is to find out how well such a `conventional' statistical classifier performs in the field of image object recognition (for future use within a content-based image retrieval system) We present a mixture density based Bayesian classifier and compare the results obtained on some well known image object recognition tasks with other state-of-the-art classifiers. Furthermore, we propose a new, robust variant of a linear discriminant analysis for feature reduction as well as a new classification approach, called the `virtual-test-sample' method, which significantly improves the recognition performance of the proposed classifier.