Maximum Membership Scale Selection

Authors:
Marco Loog;Yan Li;David M. Tax
Affiliations:
Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands 2628 CD;Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands 2628 CD;Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands 2628 CD
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

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Abstract

The use of multi-scale features is explored in the setting of supervised image segmentation by means of pixel classification. More specifically, we consider an interesting link between so-called scale selection and the maximum combination rule from pattern recognition. The parallel with scale selection is drawn further and a multi-scale segmentation method is introduced that relies on a per-scale classification followed by an over-scale fusion of these outcomes. A limited number of experiments is presented to provide some further understanding of the technique proposed.