A new decision fusion technique for image classification

Authors:
Mete Ozay;Fatos Tunay Yarman Vural
Affiliations:
Department of Computer Engineering, METU;Department of Computer Engineering, METU
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 3
Cited 0

Original Contribution: Stacked generalization

Neural Networks
Decision Fusion

Decision Fusion
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms

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Abstract

In this study, we introduce a new image classification technique using decision fusion. The proposed technique, called Meta-Fuzzified Yield Value (Meta-FYV), is based on two-layer Stacked Generalization (SG) architecture [1]. At the base-layer, the system, receives a set of feature vectors of various dimensions and dynamical ranges and outputs hypotheses through fuzzy transformations. Then, the hypotheses created by the base layer transformations are concatenated for building a regression equation at meta-layer. Experimental evidence indicates that the Meta-FYV is superior compared to one of the most successful Fuzzy SG methods, introduced by Akbas [2].