Multiple Classifier Fusion Performance in Networked Stochastic Vector Quantisers

We detail an exploratory experiment aimed at determining the performance of stochastic vector quantisation as a purely fusion methodology, in contrast to its performance as a composite classification/fusion mechanism. To achieve this we obtain an initial pattern space for which a simulated PDF is generated: a well-factored SVQ classifier then acts as a composite classifier/classifier fusion system in order to provide an overall representation rate. This performance is then contrasted with that of the individual classifiers (constituted by the factored code-vectors) acting in combination via conventional combination mechanisms. In this way, we isolate the performance of networked-SVQs as a purely combinatory mechanism for the base classifiers.

Authors and Affiliations

1. Centre for Vision, Speech and Signal Processing, Dept. of Electronic & Electrical Engineering, University of Surrey, GU2 5XH, Guildford, Surrey, United Kingdom

   R. Patenall, D. Windridge & J. Kittler

Editors and Affiliations

1. NASA Ames Research Center, Mail Stop 269-1, 94035-1000, Moffett Field, CA, USA

   Nikunj C. Oza

2. Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ, USA

   Robi Polikar

3. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK

   Josef Kittler

4. University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy

   Fabio Roli

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© 2005 Springer-Verlag Berlin Heidelberg

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