A new class of kernels for object recognition based on local image
feature representations are introduced in this paper. These kernels
satisfy the Mercer condition and incorporate multiple types of local
features and semilocal constraints between them. Experimental results
of SVM classifiers coupled with the proposed kernels are reported on
recognition tasks with the COIL-100 database and compared with
existing methods. The proposed kernels achieved competitive
performance and were robust to changes in object configurations and
image degradations.
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