A New Decision Fusion Technique for Image Classification

Ozay, Mete
Vural, Fatos Tunay Yarman
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.


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This study aims to present a sequential method for the classification of the autoregressive processes. Different from the conventional detectors having fixed sample size, the method uses Wald’s sequential probability ratio test and has a variable sample size. It is shown that the suggested method produces the classification decisions much earlier than fixed sample size alternative on the average. The proposed method is extended to the case when processes have unknown variance. The effects of the unknown pro...
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Citation Formats
M. Ozay and F. T. Y. Vural, “A New Decision Fusion Technique for Image Classification,” 2009, p. 2189, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66090.