Abstract
Standard learning techniques can be difficult to apply in a setting where instances are sets of features, varying in cardinality and with additional geometric structure. Kernel-based classification methods can be effective in this situation as they avoid explicitly representing the instances. We describe a kernel function which attempts to establish correspondences between local features while also respecting the geometric structure. We generalize some of the existing work on context dependent kernels and demonstrate a connection to popular graph kernels. We also propose an efficient computation scheme which makes the new kernel applicable to instances with hundreds of features. The kernel function is shown to be positive semidefinite, making it suitable for use in a wide range of learning algorithms.
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Semenovich, D., Sowmya, A. (2011). Geometry Aware Local Kernels for Object Recognition. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19315-6_38
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DOI: https://doi.org/10.1007/978-3-642-19315-6_38
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