The intermediate matching kernel for image local features

We introduce the intermediate matching (IM) kernel for SVM-hased object recognition. The IM kernel operates on a feature space of vector sets where each image is represented by a set of local features. Matching algorithms have proved to be efficient for such types of features. Nevertheless, kernelizing the matching for SVM does not lead to positive definite kernels. The IM kernel overcomes this drawback, as it mimics matching algorithms while being positive definite. The IM kernel introduces an intermediary set of so-called virtual local features. These select the pairs of local features to be matched. Comparisons with the matching kernel shows that the IM kernels leads to similar performances.