In this paper we discuss the mathematical properties of a few kernels specifically constructed for dealing with image data in binary classification and novelty detection problems. First, we show that histogram intersection is a Mercer’s kernel. Then, we show that a similarity measure based on the notion of Hausdorff distance and directly applicable to raw images, though not a Mercer’s kernel, is a kernel for novelty detection. Both kernels appear to be well suited for building effective vision-based learning systems.
CITATION STYLE
Barla, A., Franceschi, E., Odone, F., & Verri, A. (2002). Image kernels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2388, pp. 83–96). Springer Verlag. https://doi.org/10.1007/3-540-45665-1_7
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