Abstract
The problem of multi-modal pattern recognition is considered under the assumption that the kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output scales of single sensor is employed as an appropriate joint scale corresponding to the idea of combining modalities, actually, at the sensor level. From this point of view, the known kernel fusion techniques, including Relevance and Support Kernel Machines, offer a toolkit of combining pattern recognition modalities. We propose an SVM-based quasi-statistical approach to multi-modal pattern recognition which covers both of these modes of kernel fusion.
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Mottl, V., Tatarchuk, A., Sulimova, V., Krasotkina, O., Seredin, O. (2007). Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_1
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DOI: https://doi.org/10.1007/978-3-540-72523-7_1
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