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Technical Report Details

Date 26-11-2003
Number DISI-TR-03-12
Title Building kernels from binary strings for image matching
Authors Francesca Odone, Annalisa Barla, Alessandro Verri
Bibtex Entry
E-mail odone@disi.unige.it
Link ftp://ftp.disi.unige.it/person/OdoneF/TR-DISI-2003-12.ps
Abstract In the statistical learning framework the use of appropriate kernels may be the key for substantial improvement in solving a given problem. In essence, a kernel is a similarity measure between input points satisfying some mathematical requirements and possibly capturing the domain knowledge. In this paper we focus on kernels for images: we represent the image information content with binary strings and discuss various bitwise manipulations obtained using logical operators and convolution with non-binary stencils. In the theoretical contribution of our work we show that histogram intersection is a Mercer's kernel and we determine the modifications under which a similarity measure based on the notion of Hausdorff distance is also a Mercer's kernel. In both cases we determine explicitly the mapping from input to feature space. The presented experimental results support the relevance of our analysis for developing effective trainable systems.
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