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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