Multiple classifier fusion performance in networked stochastic vector quantisers

Authors:
R. Patenall;D. Windridge;J. Kittler
Affiliations:
Centre for Vision, Speech and Signal Processing, Dept. of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey, United Kingdom;Centre for Vision, Speech and Signal Processing, Dept. of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey, United Kingdom;Centre for Vision, Speech and Signal Processing, Dept. of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey, United Kingdom
Venue:
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Year:
2005

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Abstract

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.